"A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial
or guide to the Python language for a beginner audience. If all you know about computers is how to
save text files, then this is the book for you.

Thank you so much for writing A Byte Of Python. I just started learning how to code two days ago
and I’m already building some simple games. Your guide has been a dream and I just wanted to let
you know how valuable it has been.

— Franklin

I’m from Dayanandasagar College of Engineering (7th sem, CSE). Firstly i want to say that your book
"The byte of python" is too good a book for a beginner in python like me.The concepts are so well
explained with simple examples that helped me to easily learn python. Thank you so much.

— Madhura

I am a 18 year old IT student studying at University in Ireland. I would like to express my
gratitude to you for writing your book "A Byte of Python", I already had knowledge of 3 programming
langagues - C, Java and Javascript, and Python was by far the easiest langague I have ever learned,
and that was mainly because your book was fantastic and made learning python very simple and
interesting. It is one of the best written and easy to follow programming books I have ever
read. Congratulations and keep up the great work.

— Matt

Hi, I’m from Dominican Republic. My name is Pavel, recently I read your book A Byte of Python and
I consider it excellent!! :). I learnt much from all the examples. Your book is of great help for
newbies like me…​

I am a student from China, Now ,I have read you book A byte of Python, Oh it’s beautiful. The book
is very simple but can help all the first learnners. You know I am interesting in Java and cloud
computing many times, i have to coding programm for the server, so i think python is a good choice,
finish your book, i think its not only a good choice its must use the Python. My English is not
very well, the email to you, i just wanna thank you! Best Wishes for you and your family.

— Roy Lau

I recently finished reading Byte of Python, and I thought I really ought to thank you. I was very
sad to reach the final pages as I now have to go back to dull, tedious oreilly or etc. manuals for
learning about python. Anyway, I really appreciate your book.

Dear Swaroop, I am taking a class from an instructor that has no interest in teaching. We are using
Learning Python, second edition, by O’Reilly. It is not a text for beginner without any programming
knowledge, and an instructor that should be working in another field. Thank you very much for your
book, without it I would be clueless about Python and programming. Thanks a million, you are able
to break the message down to a level that beginners can understand and not everyone can.

I love your book! It is the greatest Python tutorial ever, and a very useful reference. Brilliant,
a true masterpiece! Keep up the good work!

— Chris-André Sommerseth

First of all, I want to say thanks to you for this greate book. I think it is a good book for those
who are looking for a beginner’s tutorial for Python.

It is about two or there years ago, I think, when I first heard of this book. At that time, I am
not able to read some book in English yet, so I got a chinese translation, which took me into the
gate of Python programming.

Recently, I reread this book. This time, of course, the english version. I couldn’t believe that I
can read the whole book without my dictionary at hand. Of course, it all dues to your effort to
make this book an easy-to-understand one.

I’m just e-mailing you to thank you for writing Byte of Python online. I had been attempting
Python for a few months prior to stumbling across your book, and although I made limited success
with pyGame, I never completed a program.

Thanks to your simplification of the categories, Python actually seems a reachable goal. It seems
like I have finally learned the foundations and I can continue into my real goal, game development.

…​

Once again, thanks VERY much for placing such a structured and helpful guide to basic programming
on the web. It shoved me into and out of OOP with an understanding where two text books had
failed.

I would like to thank you for your book A Byte of Python which i myself find the best way to
learn python. I am a 15 year old i live in egypt my name is Ahmed. Python was my second programming
language i learn visual basic 6 at school but didn’t enjoy it, however i really enjoyed learning
python. I made the addressbook program and i was sucessful. i will try to start make more programs
and read python programs (if you could tell me source that would be helpful). I will also start on
learning java and if you can tell me where to find a tutorial as good as yours for java that would
help me a lot. Thanx.

A wonderful resource for beginners wanting to learn more about Python is the 110-page PDF tutorial
A Byte of Python by Swaroop C H. It is well-written, easy to follow, and may be the best
introduction to Python programming available.

Yesterday I got through most of Byte of Python on my Nokia N800 and it’s the easiest and most
concise introduction to Python I have yet encountered. Highly recommended as a starting point for
learning Python.

Before I started to learn Python, I’ve acquired basic programming skills in Assembly, C, C++, C#
and Java. The very reason I wanted to learn Python is it’s popular (people are talking about it)
and powerful (reality). This book written by Mr. Swaroop is a very good guide for both brand-new
programmers and new python programmers. Took 10 half days to go through it. Great Help!

This book cleared up many questions I had about certain aspects of Python such as object oriented
programming.

I do not feel like an expert at OO but I know this book helped me on a first step or two.

I have now written several python programs that actually do real things for me as a system
administrator. They are all procedural oriented but they are small by most peoples standards.

Again, thanks for this book. Thank you for having it on the web.

— Bob

I just want to thank you for writing the first book on programming I’ve ever really read. Python is
now my first language, and I can just imagine all the possibilities. So thank you for giving me the
tools to create things I never would have imagined I could do before.

— The Walrus

I wanted to thank you for writing A Byte Of Python (2 & 3 Versions). It has been invaluable to
my learning experience in Python & Programming in general.

Needless to say, I am a beginner in the programming world, a couple of months of self study up to
this point. I had been using youtube tutorials & some other online tutorials including other free
books. I decided to dig into your book yesterday, & I’ve learned more on the first few pages than
any other book or tutorial. A few things I had been confused about, were cleared right up with a
GREAT example & explanation. Can’t wait to read (and learn) more!!

Thank you so much for not only writing the book, but for putting it under the creative commons
license (free). Thank goodness there are unselfish people like you out there to help & teach the
rest of us.

— Chris

I wrote you back in 2011 and I was just getting into Python and wanted to thank you for your
tutorial "A Byte of Python". Without it, I would have fallen by the wayside. Since then I have
gone on to program a number of functions in my organization with this language with yet more on the
horizon. I would not call myself an advanced programmer by any stretch but I notice the occasional
request for assistance now from others since I started using it. I discovered, while reading
"Byte" why I had ceased studying C and C[]+ and it was because the book given to me started out with
an example containing an augmented assignment. Of course, there was no explanation for this
arrangement of operators and I fell on my head trying to make sense of what was on the written
page. As I recall it was a most frustrating exercise which I eventually abandoned. Doesn't mean C
or C+ is impossible to learn, or even that I am stupid, but it does mean that the documentation I
worked my way through did not define the symbols and words which is an essential part of any
instruction. Just as computers will not be able to understand a computer word or computer symbol
that is outside the syntax for the language being used, a student new to any field will not grasp
his subject if he encounters words or symbols for which there are no definitions. You get a "blue
screen" as it were in either case. The solution is simple, though: find the word or symbol and get
the proper definition or symbol and lo and behold,the computer or student can proceed. Your book
was so well put together that I found very little in it I couldn’t grasp. So, thank you. I
encourage you to continue to include full definitions of terms. The documentation with Python is
good, once you know, (the examples are its strength from what I see) but in many cases it seems
that you have to know in order to understand the documentation which to my mind is not what
should be. Third party tutorials express the need for clarification of the documentation and their
success largely depends on the words that are used to describe the terminology. I have recommended
your book to many others. Some in Australia, some in the Caribbean and yet others in the US. It
fills a niche no others do. I hope you are doing well and wish you all the success in the future.

— Nick

hey, this is ankush(19). I was facing a great difficulty to start with python. I tried a lot of
books but all were bulkier and not target oriented; and then i found this lovely one, which made me
love python in no time. Thanks a lot for this "beautiful piece of book".

— Ankush

I would like to thank you for your excellent guide on Python. I am a molecular biologist (with
little programming background) and for my work I need to handle big datasets of DNA sequences and
to analyse microscope images. For both things, programming in python has been useful, if not
essential to complete and publish a 6-years project.

That such a guide is freely available is a clear sign that the forces of evil are not yet ruling
the world! :)

— Luca

Since this is going to be the first language you learn, you should use A Byte of Python. It really
gives a proper introduction into programming in Python and it is paced well enough for the average
beginner. The most important thing from then on will be actually starting to practice making your
own little programs.

Just to say a loud and happy thank you very much for publishing "A Byte of Python" and "A Byte of
Vim". Those books were very useful to me four or five years ago when I starting learning
programming. Right now I’m developing a project that was a dream for a long, long time and just
want to say thank you. Keep walking. You are a source of motivation. All the best.

— Jocimar

Finished reading A byte of Python in 3 days. It is thoroughly interesting. Not a single page was
boring. I want to understand the Orca screen reader code. Your book has hopefully equipped me
for it.

i’m a 4 years experienced Java&C developer from China. Recently, i want to do some work on zim-wiki
note project which uses pygtk to implement.

i read your book in 6 days, and i can read and write python code examples now.
thx for your contribution.
plz keep your enthusiasm to make this world better, this is just a little encourage from China.
Your reader
Lee

I am Isen from Taiwan, who is a graduating PhD student in Electrical Engineering Department of
National Taiwan University. I would like to thank you for your great book. I think it is not only
just easy to read but also comprehensive and complete for a new comer of Python. The reason I read
your book is that I am starting to work on the GNU Radio framework. Your book let me catch most of
important core ideas and skill of Python with a minimum time.

I also saw that you do not mind that readers send you a thank note in your book. So I really like
your book and appreciate it. Thanks.

You are free to Remix i.e. to make changes to this book (especially translations)

You are free to use it for commercial purposes

Please note:

Please do not sell electronic or printed copies of the book unless you have clearly and
prominently mentioned in the description that these copies are not from the original author of
this book.

Attribution must be shown in the introductory description and front page of the document by
linking back to http://swaroopch.com/notes/python and clearly indicating that the original text can be fetched from this
location.

All the code/scripts provided in this book is licensed under the
3-clause BSD License unless otherwise noted.

Python is probably one of the few programming languages which is both simple and powerful. This is
good for beginners as well as for experts, and more importantly, is fun to program with. This book
aims to help you learn this wonderful language and show how to get things done quickly and
painlessly - in effect 'The Anti-venom to your programming problems'.

This book serves as a guide or tutorial to the Python programming language. It is mainly targeted
at newbies. It is useful for experienced programmers as well.

The aim is that if all you know about computers is how to save text files, then you can learn
Python from this book. If you have previous programming experience, then you can also learn Python
from this book.

If you do have previous programming experience, you will be interested in the differences between
Python and your favorite programming language - I have highlighted many such differences. A little
warning though, Python is soon going to become your favorite programming language!

There are two ways of constructing a software design: one way is to make it so simple that there
are obviously no deficiencies; the other is to make it so complicated that there are no obvious
deficiencies.

— C. A. R. Hoare

Success in life is a matter not so much of talent and opportunity as of concentration and
perseverance.

Python is one of those rare languages which can claim to be both simple and powerful. You will
find yourself pleasantly surprised to see how easy it is to concentrate on the solution to the
problem rather than the syntax and structure of the language you are programming in.

The official introduction to Python is:

Python is an easy to learn, powerful programming language. It has efficient high-level data
structures and a simple but effective approach to object-oriented programming. Python’s elegant
syntax and dynamic typing, together with its interpreted nature, make it an ideal language for
scripting and rapid application development in many areas on most platforms.

I will discuss most of these features in more detail in the next section.

Story behind the name

Guido van Rossum, the creator of the Python language, named the language after the BBC show "Monty
Python’s Flying Circus". He doesn’t particularly like snakes that kill animals for food by winding
their long bodies around them and crushing them.

Python is a simple and minimalistic language. Reading a good Python program feels almost like
reading English, although very strict English! This pseudo-code nature of Python is one of its
greatest strengths. It allows you to concentrate on the solution to the problem rather than the
language itself.

Easy to Learn

As you will see, Python is extremely easy to get started with. Python has an extraordinarily simple
syntax, as already mentioned.

Free and Open Source

Python is an example of a FLOSS (Free/Libré and Open Source Software). In simple terms, you can
freely distribute copies of this software, read its source code, make changes to it, and use pieces
of it in new free programs. FLOSS is based on the concept of a community which shares
knowledge. This is one of the reasons why Python is so good - it has been created and is constantly
improved by a community who just want to see a better Python.

High-level Language

When you write programs in Python, you never need to bother about the low-level details such as
managing the memory used by your program, etc.

Portable

Due to its open-source nature, Python has been ported to (i.e. changed to make it work on) many
platforms. All your Python programs can work on any of these platforms without requiring any
changes at all if you are careful enough to avoid any system-dependent features.

You can even use a platform like Kivy to create games for your computer and for
iPhone, iPad, and Android.

Interpreted

This requires a bit of explanation.

A program written in a compiled language like C or C[]+ is converted from the source language
i.e. C or C+ into a language that is spoken by your computer (binary code i.e. 0s and 1s) using a
compiler with various flags and options. When you run the program, the linker/loader software
copies the program from hard disk to memory and starts running it.

Python, on the other hand, does not need compilation to binary. You just run the program directly
from the source code. Internally, Python converts the source code into an intermediate form called
bytecodes and then translates this into the native language of your computer and then runs it. All
this, actually, makes using Python much easier since you don’t have to worry about compiling the
program, making sure that the proper libraries are linked and loaded, etc. This also makes your
Python programs much more portable, since you can just copy your Python program onto another
computer and it just works!

Object Oriented

Python supports procedure-oriented programming as well as object-oriented programming. In
procedure-oriented languages, the program is built around procedures or functions which are
nothing but reusable pieces of programs. In object-oriented languages, the program is built
around objects which combine data and functionality. Python has a very powerful but simplistic way
of doing OOP, especially when compared to big languages like C++ or Java.

Extensible

If you need a critical piece of code to run very fast or want to have some piece of algorithm not
to be open, you can code that part of your program in C or C\++ and then use it from your Python
program.

Embeddable

You can embed Python within your C/C\++ programs to give scripting capabilities for your
program’s users.

Extensive Libraries

The Python Standard Library is huge indeed. It can help you do various things involving regular
expressions,documentation generation, unit testing, threading, databases, web browsers, CGI, FTP,
email, XML, XML-RPC, HTML, WAV files, cryptography, GUI (graphical user interfaces), and other
system-dependent stuff. Remember, all this is always available wherever Python is installed. This
is called the Batteries Included philosophy of Python.

Besides the standard library, there are various other high-quality libraries which you can find at
the Python Package Index.

Summary

Python is indeed an exciting and powerful language. It has the right combination of performance and
features that make writing programs in Python both fun and easy.

You can ignore this section if you’re not interested in the difference between "Python version 2"
and "Python version 3". But please do be aware of which version you are using. This book is written
for Python 2.

Remember that once you have properly understood and learn to use one version, you can easily learn
the differences and use the other one. The hard part is learning programming and understanding the
basics of Python language itself. That is our goal in this book, and once you have achieved that
goal, you can easily use Python 2 or Python 3 depending on your situation.

You may find it interesting to read what great hackers like ESR have to say about Python:

Eric S. Raymond is the author of "The Cathedral and the Bazaar" and is also the person who
coined the term Open Source. He says that Python has
become his favorite programming language. This article was the real inspiration for my first brush
with Python.

Bruce Eckel is the author of the famous 'Thinking in Java' and 'Thinking in C++' books. He says
that no language has made him more productive than Python. He says that Python is perhaps the only
language that focuses on making things easier for the programmer. Read the
complete interview for more details.

Peter Norvig is a well-known Lisp author and Director of Search Quality at Google (thanks to
Guido van Rossum for pointing that out). He says that
writing Python is like writing in pseudocode. He says
that Python has always been an integral part of Google. You can actually verify this statement by
looking at the Google Jobs page which lists Python knowledge
as a requirement for software engineers.

If you want to be able to use Python from the Windows command line i.e. the DOS prompt, then you
need to set the PATH variable appropriately.

For Windows 2000, XP, 2003 , click on Control Panel → System → Advanced → Environment
Variables. Click on the variable named PATH in the System Variables section, then select
Edit and add ;C:\Python27 (please verify that this folder exists, it will be different for
newer versions of Python) to the end of what is already there. Of course, use the appropriate
directory name.

For older versions of Windows, open the file C:\AUTOEXEC.BAT and add the line
PATH=%PATH%;C:\Python33 and restart the system. For Windows NT, use the AUTOEXEC.NT file.

For Windows Vista:

Click Start and choose Control Panel

Click System, on the right you’ll see "View basic information about your computer"

On the left is a list of tasks, the last of which is Advanced system settings. Click that.

The Advanced tab of the System Properties dialog box is shown. Click the Environment
Variables button on the bottom right.

In the lower box titled System Variables scroll down to Path and click the Edit button.

Change your path as need be.

Restart your system. Vista didn’t pick up the system path environment variable change until I
restarted.

For Windows 7 and 8:

Right click on Computer from your desktop and select Properties or click Start and choose
Control Panel → System and Security → System. Click on Advanced system settings on the
left and then click on the Advanced tab. At the bottom click on Environment Variables and under
System variables, look for the PATH variable, select and then press Edit.

Go to the end of the line under Variable value and append ;C:\Python27 (please verify that this
folder exists, it will be different for newer versions of Python) to the end of what is already
there. Of course, use the appropriate folder name.

If the value was %SystemRoot%\system32; It will now become %SystemRoot%\system32;C:\Python27

Click OK and you are done. No restart is required, however you may have to close and reopen the
command line.

To verify, open the terminal by opening the Terminal application or by pressing Alt+F2
and entering gnome-terminal. If that doesn’t work, please refer the documentation of your
particular GNU/Linux distribution. Now, run python and ensure there are no errors.

You can see the version of Python on the screen by running:

$ python -V
Python 2.7.6

Note

$ is the prompt of the shell. It will be different for you depending on the settings of the
operating system on your computer, hence I will indicate the prompt by just the $ symbol.

Caution

Output may be different on your computer, depending on the version of Python software
installed on your computer.

Open the terminal in your operating system (as discussed previously in the
Installation chapter) and then open the Python prompt by typing python and
pressing enter key.

Once you have started Python, you should see >>> where you can start typing stuff. This is called
the Python interpreter prompt.

At the Python interpreter prompt, type:

print"Hello World"

followed by the enter key. You should see the words Hello World printed to the screen.

Here is an example of what you should be seeing, when using a Mac OS X computer. The details about
the Python software will differ based on your computer, but the part from the prompt (i.e. from
>>> onwards) should be the same regardless of the operating system.

Notice that Python gives you the output of the line immediately! What you just entered is a single
Python statement. We use print to (unsurprisingly) print any value that you supply to it. Here,
we are supplying the text hello world and this is promptly printed to the screen.

Note

How to Quit the Interpreter Prompt

If you are using a GNU/Linux or OS X shell, you can exit the interpreter prompt by pressing
ctrl+d or entering exit() (note: remember to include the parentheses, ()) followed by
the enter key.

If you are using the Windows command prompt, press ctrl+z followed by the enter key.

We cannot type out our program at the interpreter prompt every time we want to run something, so we
have to save them in files and can run our programs any number of times.

To create our Python source files, we need an editor software where you can type and save. A good
programmer’s editor will make your life easier in writing the source files. Hence, the choice of an
editor is crucial indeed. You have to choose an editor as you would choose a car you would buy. A
good editor will help you write Python programs easily, making your journey more comfortable and
helps you reach your destination (achieve your goal) in a much faster and safer way.

One of the very basic requirements is syntax highlighting where all the different parts of your
Python program are colorized so that you can see your program and visualize its running.

If you have no idea where to start, I would recommend using
PyCharm Educational Edition software which is
available on Windows, Mac OS X and GNU/Linux. Details in the next section.

If you are using Windows, do not use Notepad - it is a bad choice because it does not do syntax
highlighting and also importantly it does not support indentation of the text which is very
important in our case as we will see later. Good editors will automatically do this.

If you are an experienced programmer, then you must be already using Vim or
Emacs. Needless to say, these are two of the most powerful
editors and you will benefit from using them to write your Python programs. I personally use both
for most of my programs, and have even written an entire book on
Vim.

In case you are willing to take the time to learn Vim or Emacs, then I highly recommend that you do
learn to use either of them as it will be very useful for you in the long run. However, as I
mentioned before, beginners can start with PyCharm and focus the learning on Python rather than the
editor at this moment.

To reiterate, please choose a proper editor - it can make writing Python programs more fun and
easy.

Change untitled to helloworld as the location of the project, you should see details similar to
this:

Click the Create button.

Right-click on the helloworld in the sidebar and select New → Python File:

You will be asked to type the name, type hello:

You can now see a file opened for you:

Delete the lines that are already present, and now type the following:

print"hello world"

Now right-click on what you typed (without selecting the text), and click on Run 'hello'.

You should now see the output (what it prints) of your program:

Phew! That was quite a few steps to get started, but henceforth, every time we ask you to create a
new file, remember to just right-click on helloworld on the left → New → Python File and
continue the same steps to type and run as shown above.

Now let’s get back to programming. There is a tradition that whenever you learn a new programming
language, the first program that you write and run is the 'Hello World' program - all it does is
just say 'Hello World' when you run it. As Simon Cozens [1] says, it is the "traditional incantation to the programming gods to help you
learn the language better."

Start your choice of editor, enter the following program and save it as hello.py.

Where should you save the file? To any folder for which you know the location of the folder. If you
don’t understand what that means, create a new folder and use that location to save and run all
your Python programs:

/tmp/py on Mac OS X

/tmp/py on GNU/Linux

C:\\py on Windows

To create the above folder (for the operating system you are using), use the mkdir command in the
terminal, for example, mkdir /tmp/py.

Important

Always ensure that you give it the file extension of .py, for example, foo.py.

To run your Python program:

Open a terminal window (see the previous Installation chapter on how to do that)

Change directory to where you saved the file, for example, cd /tmp/py

Run the program by entering the command python hello.py. The output is as shown below.

$ python hello.py
hello world

If you got the output as shown above, congratulations! - you have successfully run your first
Python program. You have successfully crossed the hardest part of learning programming, which is,
getting started with your first program!

In case you got an error, please type the above program exactly as shown above and run the
program again. Note that Python is case-sensitive i.e. print is not the same as Print - note
the lowercase p in the former and the uppercase P in the latter. Also, ensure there are no
spaces or tabs before the first character in each line - we will see why this is
important later.

How It Works

A Python program is composed of statements. In our first program, we have only one statement. In
this statement, we call the print statement to which we supply the text "hello world".

If you need quick information about any function or statement in Python, then you can use the
built-in help functionality. This is very useful especially when using the interpreter
prompt. For example, run help('len') - this displays the help for the len function which is
used to count number of items.

Tip

Press q to exit the help.

Similarly, you can obtain information about almost anything in Python. Use help() to learn more
about using help itself!

In case you need to get help for operators like return, then you need to put those inside quotes
such as help('return') so that Python doesn’t get confused on what we’re trying to do.

Just printing hello world is not enough, is it? You want to do more than that - you want to take
some input, manipulate it and get something out of it. We can achieve this in Python using
constants and variables, and we’ll learn some other concepts as well in this chapter.

An example of a literal constant is a number like 5, 1.23, or a string like 'This is a
string' or "It’s a string!".

It is called a literal because it is literal - you use its value literally. The number 2 always
represents itself and nothing else - it is a constant because its value cannot be changed. Hence,
all these are referred to as literal constants.

This means that once you have created a string, you cannot change it. Although this might seem like
a bad thing, it really isn’t. We will see why this is not a limitation in the various programs that
we see later on.

Note

Note for C/C++ Programmers

There is no separate char data type in Python. There is no real need for it and I am sure you
won’t miss it.

Note

Note for Perl/PHP Programmers

Remember that single-quoted strings and double-quoted strings are the same - they do not differ in
any way.

Sometimes we may want to construct strings from other information. This is where the format()
method is useful.

Save the following lines as a file str_format.py:

age=20name='Swaroop'print'{0} was {1} years old when he wrote this book'.format(name,age)print'Why is {0} playing with that python?'.format(name)

Output:

$ python str_format.py
Swaroop was 20 years old when he wrote this book
Why is Swaroop playing with that python?

How It Works

A string can use certain specifications and subsequently, the format method can be called to
substitute those specifications with corresponding arguments to the format method.

Observe the first usage where we use {0} and this corresponds to the variable name which is the
first argument to the format method. Similarly, the second specification is {1} corresponding to
age which is the second argument to the format method. Note that Python starts counting from 0
which means that first position is at index 0, second position is at index 1, and so on.

Notice that we could have achieved the same using string concatenation:

name+' is '+str(age)+' years old'

but that is much uglier and error-prone. Second, the conversion to string would be done
automatically by the format method instead of the explicit conversion to strings needed in this
case. Third, when using the format method, we can change the message without having to deal with
the variables used and vice-versa.

Also note that the numbers are optional, so you could have also written as:

age=20name='Swaroop'print'{} was {} years old when he wrote this book'.format(name,age)print'Why is {} playing with that python?'.format(name)

which will give the same exact output as the previous program.

What Python does in the format method is that it substitutes each argument value into the place
of the specification. There can be more detailed specifications such as:

# decimal (.) precision of 3 for float '0.333'print'{0:.3f}'.format(1.0/3)# fill with underscores (_) with the text centered# (^) to 11 width '___hello___'print'{0:_^11}'.format('hello')# keyword-based 'Swaroop wrote A Byte of Python'print'{name} wrote {book}'.format(name='Swaroop',book='A Byte of Python')

Output:

0.333
___hello___
Swaroop wrote A Byte of Python

Since we are discussing formatting, note that print always ends with an invisible "new line"
character (\n) so that repeated calls to print will all print on a separate line each. To
prevent this newline character from being printed, you can end the statement with a comma:

Suppose, you want to have a string which contains a single quote ('), how will you specify this
string? For example, the string is "What’s your name?". You cannot specify 'What’s your name?'
because Python will be confused as to where the string starts and ends. So, you will have to
specify that this single quote does not indicate the end of the string. This can be done with the
help of what is called an escape sequence. You specify the single quote as \' : notice the
backslash. Now, you can specify the string as 'What's your name?'.

Another way of specifying this specific string would be "What’s your name?" i.e. using double
quotes. Similarly, you have to use an escape sequence for using a double quote itself in a double
quoted string. Also, you have to indicate the backslash itself using the escape sequence \\.

What if you wanted to specify a two-line string? One way is to use a triple-quoted string as shown
previously or you can use an escape sequence for the newline character - \n to
indicate the start of a new line. An example is:

'This is the first line\nThis is the second line'

Another useful escape sequence to know is the tab: \t. There are many more escape sequences but I
have mentioned only the most useful ones here.

One thing to note is that in a string, a single backslash at the end of the line indicates that the
string is continued in the next line, but no newline is added. For example:

Using just literal constants can soon become boring - we need some way of storing any information
and manipulate them as well. This is where variables come into the picture. Variables are exactly
what the name implies - their value can vary, i.e., you can store anything using a
variable. Variables are just parts of your computer’s memory where you store some
information. Unlike literal constants, you need some method of accessing these variables and hence
you give them names.

Variables can hold values of different types called data types. The basic types are numbers and
strings, which we have already discussed. In later chapters, we will see how to create our own
types using classes.

# Filename : var.pyi=5printii=i+1printis='''This is a multi-line string.This is the second line.'''prints

Output:

5
6
This is a multi-line string.
This is the second line.

How It Works

Here’s how this program works. First, we assign the literal constant value 5 to the variable i
using the assignment operator (=). This line is called a statement because it states that
something should be done and in this case, we connect the variable name i to the value 5. Next,
we print the value of i using the print statement which, unsurprisingly, just prints the value
of the variable to the screen.

Then we add 1 to the value stored in i and store it back. We then print it and expectedly, we
get the value 6.

Similarly, we assign the literal string to the variable s and then print it.

Note

Note for static language programmers

Variables are used by just assigning them a value. No declaration or data type definition is
needed/used.

A physical line is what you see when you write the program. A logical line is what Python sees
as a single statement. Python implicitly assumes that each physical line corresponds to a
logical line.

An example of a logical line is a statement like print 'hello world' - if this was on a line by
itself (as you see it in an editor), then this also corresponds to a physical line.

Implicitly, Python encourages the use of a single statement per line which makes code more
readable.

If you want to specify more than one logical line on a single physical line, then you have to
explicitly specify this using a semicolon (;) which indicates the end of a logical
line/statement. For example:

i=5printi

is effectively same as

i=5;printi;

which is also same as

i=5;printi;

and same as

i=5;printi

However, I strongly recommend that you stick to writing a maximum of a single logical line on
each single physical line. The idea is that you should never use the semicolon. In fact, I have
never used or even seen a semicolon in a Python program.

There is one kind of situation where this concept is really useful: if you have a long line of
code, you can break it into multiple physical lines by using the backslash. This is referred to as
explicit line joining:

s='This is a string. \This continues the string.'prints

Output:

This is a string. This continues the string.

Similarly,

print \
i

is the same as

printi

Sometimes, there is an implicit assumption where you don’t need to use a backslash. This is the
case where the logical line has a starting parentheses, starting square brackets or a starting
curly braces but not an ending one. This is called implicit line joining. You can see this in
action when we write programs using lists in later chapters.

Whitespace is important in Python. Actually, whitespace at the beginning of the line is
important. This is called indentation. Leading whitespace (spaces and tabs) at the beginning of
the logical line is used to determine the indentation level of the logical line, which in turn is
used to determine the grouping of statements.

This means that statements which go together must have the same indentation. Each such set of
statements is called a block. We will see examples of how blocks are important in later chapters.

One thing you should remember is that wrong indentation can give rise to errors. For example:

i=5# Error below! Notice a single space at the start of the lineprint'Value is ',iprint'I repeat, the value is ',i

Notice that there is a single space at the beginning of the second line. The error indicated by
Python tells us that the syntax of the program is invalid i.e. the program was not properly
written. What this means to you is that you cannot arbitrarily start new blocks of statements
(except for the default main block which you have been using all along, of course). Cases where you
can use new blocks will be detailed in later chapters such as the Control Flow.

How to indent

Use four spaces for indentation. This is the official Python language recommendation. Good editors
will automatically do this for you. Make sure you use a consistent number of spaces for
indentation, otherwise your program will show errors.

Note

Note to static language programmers

Python will always use indentation for blocks and will never use braces. Run from future
import braces to learn more.

Now that we have gone through many nitty-gritty details, we can move on to more interesting stuff
such as control flow statements. Be sure to become comfortable with what you have read in this
chapter.

Most statements (logical lines) that you write will contain expressions. A simple example of an
expression is 2 + 3. An expression can be broken down into operators and operands.

Operators are functionality that do something and can be represented by symbols such as + or by
special keywords. Operators require some data to operate on and such data is called operands. In
this case, 2 and 3 are the operands.

Returns whether x is less than y. All comparison operators return True or False. Note the
capitalization of these names.

5 < 3 gives False and 3 < 5 gives True.

Comparisons can be chained arbitrarily: 3 < 5 < 7 gives True.

> (greater than)

Returns whether x is greater than y

5 > 3 returns True. If both operands are numbers, they are first converted to a common
type. Otherwise, it always returns False.

⇐ (less than or equal to)

Returns whether x is less than or equal to y

x = 3; y = 6; x ⇐ y returns True.

>= (greater than or equal to)

Returns whether x is greater than or equal to y

x = 4; y = 3; x >= 3 returns True.

== (equal to)

Compares if the objects are equal

x = 2; y = 2; x == y returns True.

x = 'str'; y = 'stR'; x == y returns False.

x = 'str'; y = 'str'; x == y returns True.

!= (not equal to)

Compares if the objects are not equal

x = 2; y = 3; x != y returns True.

not (boolean NOT)

If x is True, it returns False. If x is False, it returns True.

x = True; not x returns False.

and (boolean AND)

x and y returns False if x is False, else it returns evaluation of y

x = False; y = True; x and y returns False since x is False. In this case, Python will not
evaluate y since it knows that the left hand side of the 'and' expression is False which implies
that the whole expression will be False irrespective of the other values. This is called
short-circuit evaluation.

If you had an expression such as 2 + 3 * 4, is the addition done first or the multiplication? Our
high school maths tells us that the multiplication should be done first. This means that the
multiplication operator has higher precedence than the addition operator.

The following table gives the precedence table for Python, from the lowest precedence (least
binding) to the highest precedence (most binding). This means that in a given expression, Python
will first evaluate the operators and expressions lower in the table before the ones listed higher
in the table.

The following table, taken from the
Python reference manual,
is provided for the sake of completeness. It is far better to use parentheses to group operators
and operands appropriately in order to explicitly specify the precedence. This makes the program
more readable. See Changing the Order of Evaluation below for
details.

To make the expressions more readable, we can use parentheses. For example, 2 + (3 * 4) is
definitely easier to understand than 2 + 3 * 4 which requires knowledge of the operator
precedences. As with everything else, the parentheses should be used reasonably (do not overdo it)
and should not be redundant, as in (2 + (3 * 4)).

There is an additional advantage to using parentheses - it helps us to change the order of
evaluation. For example, if you want addition to be evaluated before multiplication in an
expression, then you can write something like (2 + 3) * 4.

Operators are usually associated from left to right. This means that operators with the same
precedence are evaluated in a left to right manner. For example, 2 + 3 + 4 is evaluated as (2
3) + 4. Some operators like assignment operators have right to left associativity i.e. a = b = c
is treated as a = (b = c).

The length and breadth of the rectangle are stored in variables by the same name. We use these to
calculate the area and perimeter of the rectangle with the help of expressions. We store the result
of the expression length * breadth in the variable area and then print it using the print
function. In the second case, we directly use the value of the expression 2 * (length + breadth)
in the print statement.

Also, notice how Python pretty-prints the output. Even though we have not specified a space
between 'Area is' and the variable area, Python puts it for us so that we get a clean nice
output and the program is much more readable this way (since we don’t need to worry about spacing
in the strings we use for output). This is an example of how Python makes life easy for the
programmer.

In the programs we have seen till now, there has always been a series of statements faithfully
executed by Python in exact top-down order. What if you wanted to change the flow of how it works?
For example, you want the program to take some decisions and do different things depending on
different situations, such as printing 'Good Morning' or 'Good Evening' depending on the time of
the day?

As you might have guessed, this is achieved using control flow statements. There are three control
flow statements in Python - if, for and while.

The if statement is used to check a condition: if the condition is true, we run a block of
statements (called the if-block), else we process another block of statements (called the
else-block). The else clause is optional.

Example (save as if.py):

number=23guess=int(raw_input('Enter an integer : '))ifguess==number:# New block starts hereprint'Congratulations, you guessed it.'print'(but you do not win any prizes!)'# New block ends hereelifguess<number:# Another blockprint'No, it is a little higher than that'# You can do whatever you want in a block ...else:print'No, it is a little lower than that'# you must have guessed > number to reach hereprint'Done'# This last statement is always executed,# after the if statement is executed.

Output:

$ python if.py
Enter an integer : 50
No, it is a little lower than that
Done
$ python if.py
Enter an integer : 22
No, it is a little higher than that
Done
$ python if.py
Enter an integer : 23
Congratulations, you guessed it.
(but you do not win any prizes!)
Done

How It Works

In this program, we take guesses from the user and check if it is the number that we have. We set
the variable number to any integer we want, say 23. Then, we take the user’s guess using the
raw_input() function. Functions are just reusable pieces of programs. We’ll read more about them
in the next chapter.

We supply a string to the built-in raw_input function which prints it to the screen and waits for
input from the user. Once we enter something and press enter key, the raw_input() function
returns what we entered, as a string. We then convert this string to an integer using int and
then store it in the variable guess. Actually, the int is a class but all you need to know
right now is that you can use it to convert a string to an integer (assuming the string contains a
valid integer in the text).

Next, we compare the guess of the user with the number we have chosen. If they are equal, we print
a success message. Notice that we use indentation levels to tell Python which statements belong to
which block. This is why indentation is so important in Python. I hope you are sticking to the
"consistent indentation" rule. Are you?

Notice how the if statement contains a colon at the end - we are indicating to Python that a
block of statements follows.

Then, we check if the guess is less than the number, and if so, we inform the user that they must
guess a little higher than that. What we have used here is the elif clause which actually
combines two related if else-if else statements into one combined if-elif-else statement. This
makes the program easier and reduces the amount of indentation required.

The elif and else statements must also have a colon at the end of the logical line followed by
their corresponding block of statements (with proper indentation, of course)

You can have another if statement inside the if-block of an if statement and so on - this is
called a nested if statement.

Remember that the elif and else parts are optional. A minimal valid if statement is:

ifTrue:print'Yes, it is true'

After Python has finished executing the complete if statement along with the associated elif
and else clauses, it moves on to the next statement in the block containing the if
statement. In this case, it is the main block (where execution of the program starts), and the next
statement is the print 'Done' statement. After this, Python sees the ends of the program and
simply finishes up.

Even though this is a very simple program, I have been pointing out a lot of things that you should
notice. All these are pretty straightforward (and surprisingly simple for those of you from C/C++
backgrounds). You will need to become aware of all these things initially, but after some practice
you will become comfortable with them, and it will all feel 'natural' to you.

Note

Note for C/C++ Programmers

There is no switch statement in Python. You can use an if..elif..else statement to do the same
thing (and in some cases, use a dictionary to do it quickly)

The while statement allows you to repeatedly execute a block of statements as long as a condition
is true. A while statement is an example of what is called a looping statement. A while
statement can have an optional else clause.

Example (save as while.py):

number=23running=Truewhilerunning:guess=int(raw_input('Enter an integer : '))ifguess==number:print'Congratulations, you guessed it.'# this causes the while loop to stoprunning=Falseelifguess<number:print'No, it is a little higher than that.'else:print'No, it is a little lower than that.'else:print'The while loop is over.'# Do anything else you want to do hereprint'Done'

Output:

$ python while.py
Enter an integer : 50
No, it is a little lower than that.
Enter an integer : 22
No, it is a little higher than that.
Enter an integer : 23
Congratulations, you guessed it.
The while loop is over.
Done

How It Works

In this program, we are still playing the guessing game, but the advantage is that the user is
allowed to keep guessing until he guesses correctly - there is no need to repeatedly run the
program for each guess, as we have done in the previous section. This aptly demonstrates the use of
the while statement.

We move the raw_input and if statements to inside the while loop and set the variable
running to True before the while loop. First, we check if the variable running is True and
then proceed to execute the corresponding while-block. After this block is executed, the
condition is again checked which in this case is the running variable. If it is true, we execute
the while-block again, else we continue to execute the optional else-block and then continue to the
next statement.

The else block is executed when the while loop condition becomes False - this may even be the
first time that the condition is checked. If there is an else clause for a while loop, it is
always executed unless you break out of the loop with a break statement.

The True and False are called Boolean types and you can consider them to be equivalent to the
value 1 and 0 respectively.

The for..in statement is another looping statement which iterates over a sequence of objects
i.e. go through each item in a sequence. We will see more about sequences in detail in
later chapters. What you need to know right now is that a sequence is just an ordered collection of
items.

Example (save as for.py):

foriinrange(1,5):printielse:print'The for loop is over'

Output:

$ python for.py
1
2
3
4
The for loop is over

How It Works

In this program, we are printing a sequence of numbers. We generate this sequence of numbers
using the built-in range function.

What we do here is supply it two numbers and range returns a sequence of numbers starting from
the first number and up to the second number. For example, range(1,5) gives the sequence [1, 2,
3, 4]. By default, range takes a step count of 1. If we supply a third number to range, then
that becomes the step count. For example, range(1,5,2) gives [1,3]. Remember that the range
extends up to the second number i.e. it does not include the second number.

Note that range() generates a sequence of numbers, but it will generate only one number at a
time, when the for loop requests for the next item. If you want to see the full sequence of numbers
immediately, use list(range()). Lists are explained in the data structures
chapter.

The for loop then iterates over this range - for i in range(1,5) is equivalent to for i in [1,
2, 3, 4] which is like assigning each number (or object) in the sequence to i, one at a time, and
then executing the block of statements for each value of i. In this case, we just print the
value in the block of statements.

Remember that the else part is optional. When included, it is always executed once after the
for loop is over unless a break statement is encountered.

Remember that the for..in loop works for any sequence. Here, we have a list of numbers generated
by the built-in range function, but in general we can use any kind of sequence of any kind of
objects! We will explore this idea in detail in later chapters.

Note

Note for C/C++/Java/C# Programmers

The Python for loop is radically different from the C/C++ for loop. C# programmers will note
that the for loop in Python is similar to the foreach loop in C#. Java programmers will note
that the same is similar to for (int i : IntArray) in Java 1.5.

In C/C, if you want to write `for (int i = 0; i < 5; i), then in Python you write just `for i
in range(0,5). As you can see, the for loop is simpler, more expressive and less error prone in
Python.

The break statement is used to break out of a loop statement i.e. stop the execution of a
looping statement, even if the loop condition has not become False or the sequence of items has
not been completely iterated over.

An important note is that if you break out of a for or while loop, any corresponding loop
else block is not executed.

$ python break.py
Enter something : Programming is fun
Length of the string is 18
Enter something : When the work is done
Length of the string is 21
Enter something : if you wanna make your work also fun:
Length of the string is 37
Enter something : use Python!
Length of the string is 11
Enter something : quit
Done

How It Works

In this program, we repeatedly take the user’s input and print the length of each input each
time. We are providing a special condition to stop the program by checking if the user input is
'quit'. We stop the program by breaking out of the loop and reach the end of the program.

The length of the input string can be found out using the built-in len function.

Remember that the break statement can be used with the for loop as well.

Swaroop’s Poetic Python

The input I have used here is a mini poem I have written:

Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!

In this program, we accept input from the user, but we process the input string only if it is at
least 3 characters long. So, we use the built-in len function to get the length and if the length
is less than 3, we skip the rest of the statements in the block by using the continue
statement. Otherwise, the rest of the statements in the loop are executed, doing any kind of
processing we want to do here.

We have seen how to use the three control flow statements - if, while and for along with
their associated break and continue statements. These are some of the most commonly used parts
of Python and hence, becoming comfortable with them is essential.

Functions are reusable pieces of programs. They allow you to give a name to a block of statements,
allowing you to run that block using the specified name anywhere in your program and any number of
times. This is known as calling the function. We have already used many built-in functions such
as len and range.

The function concept is probably the most important building block of any non-trivial software
(in any programming language), so we will explore various aspects of functions in this chapter.

Functions are defined using the def keyword. After this keyword comes an identifier name for
the function, followed by a pair of parentheses which may enclose some names of variables, and by
the final colon that ends the line. Next follows the block of statements that are part of this
function. An example will show that this is actually very simple:

Example (save as function1.py):

defsay_hello():# block belonging to the functionprint'hello world'# End of functionsay_hello()# call the functionsay_hello()# call the function again

Output:

$ python function1.py
hello world
hello world

How It Works

We define a function called say_hello using the syntax as explained above. This function takes no
parameters and hence there are no variables declared in the parentheses. Parameters to functions
are just input to the function so that we can pass in different values to it and get back
corresponding results.

Notice that we can call the same function twice which means we do not have to write the same code
again.

A function can take parameters, which are values you supply to the function so that the function
can do something utilising those values. These parameters are just like variables except that the
values of these variables are defined when we call the function and are already assigned values
when the function runs.

Parameters are specified within the pair of parentheses in the function definition, separated by
commas. When we call the function, we supply the values in the same way. Note the terminology
used - the names given in the function definition are called parameters whereas the values you
supply in the function call are called arguments.

Here, we define a function called print_max that uses two parameters called a and b. We find
out the greater number using a simple if..else statement and then print the bigger number.

The first time we call the function print_max, we directly supply the numbers as arguments. In
the second case, we call the function with variables as arguments. print_max(x, y) causes the
value of argument x to be assigned to parameter a and the value of argument y to be assigned
to parameter b. The printMax function works the same way in both cases.

When you declare variables inside a function definition, they are not related in any way to other
variables with the same names used outside the function - i.e. variable names are local to the
function. This is called the scope of the variable. All variables have the scope of the block
they are declared in starting from the point of definition of the name.

If you want to assign a value to a name defined at the top level of the program (i.e. not inside
any kind of scope such as functions or classes), then you have to tell Python that the name is not
local, but it is global. We do this using the global statement. It is impossible to assign a
value to a variable defined outside a function without the global statement.

You can use the values of such variables defined outside the function (assuming there is no
variable with the same name within the function). However, this is not encouraged and should be
avoided since it becomes unclear to the reader of the program as to where that variable’s
definition is. Using the global statement makes it amply clear that the variable is defined in an
outermost block.

For some functions, you may want to make some parameters optional and use default values in case
the user does not want to provide values for them. This is done with the help of default argument
values. You can specify default argument values for parameters by appending to the parameter name
in the function definition the assignment operator (=) followed by the default value.

Note that the default argument value should be a constant. More precisely, the default argument
value should be immutable - this is explained in detail in later chapters. For now, just remember
this.

Example (save as function_default.py):

defsay(message,times=1):printmessage*timessay('Hello')say('World',5)

Output:

$ python function_default.py
Hello
WorldWorldWorldWorldWorld

How It Works

The function named say is used to print a string as many times as specified. If we don’t supply a
value, then by default, the string is printed just once. We achieve this by specifying a default
argument value of 1 to the parameter times.

In the first usage of say, we supply only the string and it prints the string once. In the second
usage of say, we supply both the string and an argument 5 stating that we want to say the
string message 5 times.

Caution

Only those parameters which are at the end of the parameter list can be given default argument
values i.e. you cannot have a parameter with a default argument value preceding a parameter without
a default argument value in the function’s parameter list.

This is because the values are assigned to the parameters by position. For example,def func(a,
b=5) is valid, but def func(a=5, b) is not valid.

If you have some functions with many parameters and you want to specify only some of them, then you
can give values for such parameters by naming them - this is called keyword arguments - we use
the name (keyword) instead of the position (which we have been using all along) to specify the
arguments to the function.

There are two advantages - one, using the function is easier since we do not need to worry about
the order of the arguments. Two, we can give values to only those parameters to which we want to,
provided that the other parameters have default argument values.

$ python function_keyword.py
a is 3 and b is 7 and c is 10
a is 25 and b is 5 and c is 24
a is 100 and b is 5 and c is 50

How It Works

The function named func has one parameter without a default argument value, followed by two
parameters with default argument values.

In the first usage, func(3, 7), the parameter a gets the value 3, the parameter b gets the
value 7 and c gets the default value of 10.

In the second usage func(25, c=24), the variable a gets the value of 25 due to the position of
the argument. Then, the parameter c gets the value of 24 due to naming i.e. keyword
arguments. The variable b gets the default value of 5.

In the third usage func(c=50, a=100), we use keyword arguments for all specified values. Notice
that we are specifying the value for parameter c before that for a even though a is defined
before c in the function definition.

The maximum function returns the maximum of the parameters, in this case the numbers supplied to
the function. It uses a simple if..else statement to find the greater value and then returns
that value.

Note that a return statement without a value is equivalent to return None. None is a special
type in Python that represents nothingness. For example, it is used to indicate that a variable has
no value if it has a value of None.

Every function implicitly contains a return None statement at the end unless you have written
your own return statement. You can see this by running print some_function() where the function
some_function does not use the return statement such as:

defsome_function():pass

The pass statement is used in Python to indicate an empty block of statements.

Tip

There is a built-in function called max that already implements the 'find maximum'
functionality, so use this built-in function whenever possible.

Python has a nifty feature called documentation strings, usually referred to by its shorter name
docstrings. DocStrings are an important tool that you should make use of since it helps to
document the program better and makes it easier to understand. Amazingly, we can even get the
docstring back from, say a function, when the program is actually running!

Example (save as function_docstring.py):

defprint_max(x,y):'''Prints the maximum of two numbers. The two values must be integers.'''# convert to integers, if possiblex=int(x)y=int(y)ifx>y:printx,'is maximum'else:printy,'is maximum'print_max(3,5)printprint_max.__doc__

Output:

$ python function_docstring.py
5 is maximum
Prints the maximum of two numbers.
The two values must be integers.

How It Works

A string on the first logical line of a function is the docstring for that function. Note that
DocStrings also apply to modules and classes which we will learn about in the
respective chapters.

The convention followed for a docstring is a multi-line string where the first line starts with a
capital letter and ends with a dot. Then the second line is blank followed by any detailed
explanation starting from the third line. You are strongly advised to follow this convention for
all your docstrings for all your non-trivial functions.

We can access the docstring of the print_max function using the doc (notice the double
underscores) attribute (name belonging to) of the function. Just remember that Python treats
everything as an object and this includes functions. We’ll learn more about objects in the
chapter on classes.

If you have used help() in Python, then you have already seen the
usage of docstrings! What it does is just fetch the doc
attribute of that function and displays it in a neat manner for
you. You can try it out on the function above - just include help(print_max) in your
program. Remember to press the q key to exit help.

Automated tools can retrieve the documentation from your program in this manner. Therefore, I
strongly recommend that you use docstrings for any non-trivial function that you write. The
pydoc command that comes with your Python distribution works similarly to help() using
docstrings.

We have seen so many aspects of functions but note that we still haven’t covered all aspects of
them. However, we have already covered most of what you’ll use regarding Python functions on an
everyday basis.

You have seen how you can reuse code in your program by defining functions once. What if you wanted
to reuse a number of functions in other programs that you write? As you might have guessed, the
answer is modules.

There are various methods of writing modules, but the simplest way is to create a file with a .py
extension that contains functions and variables.

Another method is to write the modules in the native language in which the Python interpreter
itself was written. For example, you can write modules in the C
programming language and when compiled, they can be used from your Python code when using the
standard Python interpreter.

A module can be imported by another program to make use of its functionality. This is how we can
use the Python standard library as well. First, we will see how to use the standard library
modules.

$ python module_using_sys.py we are arguments
The command line arguments are:
module_using_sys.py
we
are
arguments
The PYTHONPATH is ['/tmp/py',
# many entries here, not shown here
'/Library/Python/2.7/site-packages',
'/usr/local/lib/python2.7/site-packages']

How It Works

First, we import the sys module using the import statement. Basically, this translates to us
telling Python that we want to use this module. The sys module contains functionality related to
the Python interpreter and its environment i.e. the system.

When Python executes the import sys statement, it looks for the sys module. In this case, it is
one of the built-in modules, and hence Python knows where to find it.

If it was not a compiled module i.e. a module written in Python, then the Python interpreter will
search for it in the directories listed in its sys.path variable. If the module is found, then
the statements in the body of that module are run and the module is made available for you
to use. Note that the initialization is done only the first time that we import a module.

The argv variable in the sys module is accessed using the dotted notation i.e. sys.argv. It
clearly indicates that this name is part of the sys module. Another advantage of this approach is
that the name does not clash with any argv variable used in your program.

The sys.argv variable is a list of strings (lists are explained in detail in a
later chapter. Specifically, the sys.argv contains the list of command line
arguments i.e. the arguments passed to your program using the command line.

If you are using an IDE to write and run these programs, look for a way to specify command line
arguments to the program in the menus.

Here, when we execute python module_using_sys.py we are arguments, we run the module
module_using_sys.py with the python command and the other things that follow are arguments
passed to the program. Python stores the command line arguments in the sys.argv variable for us
to use.

Remember, the name of the script running is always the first argument in the sys.argv list. So,
in this case we will have 'module_using_sys.py' as sys.argv[0], 'we' as sys.argv[1],
'are' as sys.argv[2] and 'arguments' as sys.argv[3]. Notice that Python starts counting
from 0 and not 1.

The sys.path contains the list of directory names where modules are imported from. Observe that
the first string in sys.path is empty - this empty string indicates that the current directory is
also part of the sys.path which is same as the PYTHONPATH environment variable. This means that
you can directly import modules located in the current directory. Otherwise, you will have to place
your module in one of the directories listed in sys.path.

Note that the current directory is the directory from which the program is launched. Run import
os; print os.getcwd() to find out the current directory of your program.

Importing a module is a relatively costly affair, so Python does some tricks to make it faster. One
way is to create byte-compiled files with the extension .pyc which is an intermediate form that
Python transforms the program into (remember the introduction section on how Python
works?). This .pyc file is useful when you import the module the next time from a different
program - it will be much faster since a portion of the processing required in importing a module
is already done. Also, these byte-compiled files are platform-independent.

Note

These .pyc files are usually created in the same directory as the corresponding .py
files. If Python does not have permission to write to files in that directory, then the .pyc
files will not be created.

Every module has a name and statements in a module can find out the name of their module. This is
handy for the particular purpose of figuring out whether the module is being run standalone or
being imported. As mentioned previously, when a module is imported for the first time, the code it
contains gets executed. We can use this to make the module behave in different ways depending on
whether it is being used by itself or being imported from another module. This can be achieved
using the name attribute of the module.

Example (save as module_using_name.py):

if__name__=='__main__':print'This program is being run by itself'else:print'I am being imported from another module'

Output:

$ python module_using_name.py
This program is being run by itself
$ python
>>> import module_using_name
I am being imported from another module
>>>

How It Works

Every Python module has its name defined. If this is 'main', that implies that the
module is being run standalone by the user and we can take appropriate actions.

Creating your own modules is easy, you’ve been doing it all along! This is because every Python
program is also a module. You just have to make sure it has a .py extension. The following
example should make it clear.

Example (save as mymodule.py):

defsay_hi():print'Hi, this is mymodule speaking.'__version__='0.1'

The above was a sample module. As you can see, there is nothing particularly special about it
compared to our usual Python program. We will next see how to use this module in our other Python
programs.

Remember that the module should be placed either in the same directory as the program from which we
import it, or in one of the directories listed in sys.path.

Another module (save as mymodule_demo.py):

importmymodulemymodule.say_hi()print'Version',mymodule.__version__

Output:

$ python mymodule_demo.py
Hi, this is mymodule speaking.
Version 0.1

How It Works

Notice that we use the same dotted notation to access members of the module. Python makes good
reuse of the same notation to give the distinctive 'Pythonic' feel to it so that we don’t have to
keep learning new ways to do things.

Here is a version utilising the from..import syntax (save as mymodule_demo2.py):

The output of mymodule_demo2.py is same as the output of mymodule_demo.py.

Notice that if there was already a version name declared in the module that imports mymodule,
there would be a clash. This is also likely because it is common practice for each module to
declare it’s version number using this name. Hence, it is always recommended to prefer the import
statement even though it might make your program a little longer.

You could also use:

frommymoduleimport*

This will import all public names such as say_hi but would not import version because it
starts with double underscores.

Warning

Remember that you should avoid using import-star, i.e. from mymodule import *.

Zen of Python

One of Python’s guiding principles is that "Explicit is better than Implicit". Run import this in
Python to learn more and see this
StackOverflow discussion which lists examples for each of the principles.

You can use the built-in dir function to list the identifiers that an object defines. For
example, for a module, the identifiers include the functions, classes and variables defined in that
module.

When you supply a module name to the`dir()` function, it returns the list of the names defined in
that module. When no argument is applied to it, it returns the list of names defined in the current
module.

First, we see the usage of dir on the imported sys module. We can see the huge list of
attributes that it contains.

Next, we use the dir function without passing parameters to it. By default, it returns the list
of attributes for the current module. Notice that the list of imported modules is also part of this
list.

In order to observe the dir in action, we define a new variable a and assign it a value and
then check dir and we observe that there is an additional value in the list of the same name. We
remove the variable/attribute of the current module using the del statement and the change is
reflected again in the output of the dir function.

A note on del - this statement is used to delete a variable/name and after the statement has
run, in this case del a, you can no longer access the variable a - it is as if it never existed
before at all.

Note that the dir() function works on any object. For example, run dir(print) to learn
about the attributes of the print function, or dir(str) for the attributes of the str class.

There is also a vars() function which can
potentially give you the attributes and their values, but it will not work for all cases.

By now, you must have started observing the hierarchy of organizing your programs. Variables
usually go inside functions. Functions and global variables usually go inside modules. What if you
wanted to organize modules? That’s where packages come into the picture.

Packages are just folders of modules with a special init.py file that indicates to Python
that this folder is special because it contains Python modules.

Let’s say you want to create a package called 'world' with subpackages 'asia', 'africa', etc. and
these subpackages in turn contain modules like 'india', 'madagascar', etc.

Just like functions are reusable parts of programs, modules are reusable programs. Packages are
another hierarchy to organize modules. The standard library that comes with Python is an example of
such a set of packages and modules.

We have seen how to use these modules and create our own modules.

Next, we will learn about some interesting concepts called data structures.

A list is a data structure that holds an ordered collection of items i.e. you can store a
sequence of items in a list. This is easy to imagine if you can think of a shopping list where
you have a list of items to buy, except that you probably have each item on a separate line in your
shopping list whereas in Python you put commas in between them.

The list of items should be enclosed in square brackets so that Python understands that you are
specifying a list. Once you have created a list, you can add, remove or search for items in the
list. Since we can add and remove items, we say that a list is a mutable data type i.e. this type
can be altered.

Although I’ve been generally delaying the discussion of objects and classes till now, a little
explanation is needed right now so that you can understand lists better. We will explore this topic
in detail in a later chapter.

A list is an example of usage of objects and classes. When we use a variable i and assign a value
to it, say integer 5 to it, you can think of it as creating an object (i.e. instance) i of
class (i.e. type) int. In fact, you can read help(int) to understand this better.

A class can also have methods i.e. functions defined for use with respect to that class only. You
can use these pieces of functionality only when you have an object of that class. For example,
Python provides an append method for the list class which allows you to add an item to the end
of the list. For example, mylist.append('an item') will add that string to the list
mylist. Note the use of dotted notation for accessing methods of the objects.

A class can also have fields which are nothing but variables defined for use with respect to that
class only. You can use these variables/names only when you have an object of that class. Fields
are also accessed by the dotted notation, for example, mylist.field.

Example (save as ds_using_list.py):

# This is my shopping listshoplist=['apple','mango','carrot','banana']print'I have',len(shoplist),'items to purchase.'print'These items are:',foriteminshoplist:printitem,print'\nI also have to buy rice.'shoplist.append('rice')print'My shopping list is now',shoplistprint'I will sort my list now'shoplist.sort()print'Sorted shopping list is',shoplistprint'The first item I will buy is',shoplist[0]olditem=shoplist[0]delshoplist[0]print'I bought the',olditemprint'My shopping list is now',shoplist

Output:

$ python ds_using_list.py
I have 4 items to purchase.
These items are: apple mango carrot banana
I also have to buy rice.
My shopping list is now ['apple', 'mango', 'carrot', 'banana', 'rice']
I will sort my list now
Sorted shopping list is ['apple', 'banana', 'carrot', 'mango', 'rice']
The first item I will buy is apple
I bought the apple
My shopping list is now ['banana', 'carrot', 'mango', 'rice']

How It Works

The variable shoplist is a shopping list for someone who is going to the market. In shoplist,
we only store strings of the names of the items to buy but you can add any kind of object to a
list including numbers and even other lists.

We have also used the for..in loop to iterate through the items of the list. By now, you must
have realised that a list is also a sequence. The speciality of sequences will be discussed in a
later section.

Notice the use of the trailing comma in the print statement to indicate that we want to end the
output with a space instead of the usual line break. Think of the comma as telling Python that we
have more items to print on the same line.

Next, we add an item to the list using the append method of the list object, as already discussed
before. Then, we check that the item has been indeed added to the list by printing the contents of
the list by simply passing the list to the print statement which prints it neatly.

Then, we sort the list by using the sort method of the list. It is important to understand that
this method affects the list itself and does not return a modified list - this is different from
the way strings work. This is what we mean by saying that lists are mutable and that strings are
immutable.

Next, when we finish buying an item in the market, we want to remove it from the list. We achieve
this by using the del statement. Here, we mention which item of the list we want to remove and
the del statement removes it from the list for us. We specify that we want to remove the first
item from the list and hence we use del shoplist[0] (remember that Python starts counting from
0).

If you want to know all the methods defined by the list object, see help(list) for details.

Tuples are used to hold together multiple objects. Think of them as similar to lists, but without
the extensive functionality that the list class gives you. One major feature of tuples is that they
are immutable like strings i.e. you cannot modify tuples.

Tuples are defined by specifying items separated by commas within an optional pair of parentheses.

Tuples are usually used in cases where a statement or a user-defined function can safely assume
that the collection of values i.e. the tuple of values used will not change.

Example (save as ds_using_tuple.py):

# I would recommend always using parentheses# to indicate start and end of tuple# even though parentheses are optional.# Explicit is better than implicit.zoo=('python','elephant','penguin')print'Number of animals in the zoo is',len(zoo)new_zoo='monkey','camel',zooprint'Number of cages in the new zoo is',len(new_zoo)print'All animals in new zoo are',new_zooprint'Animals brought from old zoo are',new_zoo[2]print'Last animal brought from old zoo is',new_zoo[2][2]print'Number of animals in the new zoo is', \
len(new_zoo)-1+len(new_zoo[2])

Output:

$ python ds_using_tuple.py
Number of animals in the zoo is 3
Number of cages in the new zoo is 3
All animals in new zoo are ('monkey', 'camel', ('python', 'elephant', 'penguin'))
Animals brought from old zoo are ('python', 'elephant', 'penguin')
Last animal brought from old zoo is penguin
Number of animals in the new zoo is 5

How It Works

The variable zoo refers to a tuple of items. We see that the len function can be used to get
the length of the tuple. This also indicates that a tuple is a sequence as well.

We are now shifting these animals to a new zoo since the old zoo is being closed. Therefore, the
new_zoo tuple contains some animals which are already there along with the animals brought over
from the old zoo. Back to reality, note that a tuple within a tuple does not lose its identity.

We can access the items in the tuple by specifying the item’s position within a pair of square
brackets just like we did for lists. This is called the indexing operator. We access the third
item in new_zoo by specifying new_zoo[2] and we access the third item within the third item in
the new_zoo tuple by specifying new_zoo[2][2]. This is pretty simple once you’ve understood the
idiom.

Note

Tuple with 0 or 1 items

An empty tuple is constructed by an empty pair of parentheses such as myempty = (). However, a
tuple with a single item is not so simple. You have to specify it using a comma following the first
(and only) item so that Python can differentiate between a tuple and a pair of parentheses
surrounding the object in an expression i.e. you have to specify singleton = (2 , ) if you mean
you want a tuple containing the item 2.

Note

Note for Perl programmers

A list within a list does not lose its identity i.e. lists are not flattened as in Perl. The same
applies to a tuple within a tuple, or a tuple within a list, or a list within a tuple, etc. As far
as Python is concerned, they are just objects stored using another object, that’s all.

A dictionary is like an address-book where you can find the address or contact details of a person
by knowing only his/her name i.e. we associate keys (name) with values (details). Note that the
key must be unique just like you cannot find out the correct information if you have two persons
with the exact same name.

Note that you can use only immutable objects (like strings) for the keys of a dictionary but you
can use either immutable or mutable objects for the values of the dictionary. This basically
translates to say that you should use only simple objects for keys.

Pairs of keys and values are specified in a dictionary by using the notation d = {key1 : value1,
key2 : value2 }. Notice that the key-value pairs are separated by a colon and the pairs are
separated themselves by commas and all this is enclosed in a pair of curly braces.

Remember that key-value pairs in a dictionary are not ordered in any manner. If you want a
particular order, then you will have to sort them yourself before using it.

The dictionaries that you will be using are instances/objects of the dict class.

Example (save as ds_using_dict.py):

# 'ab' is short for 'a'ddress'b'ookab={'Swaroop':'swaroop@swaroopch.com','Larry':'larry@wall.org','Matsumoto':'matz@ruby-lang.org','Spammer':'spammer@hotmail.com'}print"Swaroop's address is",ab['Swaroop']# Deleting a key-value pairdelab['Spammer']print'\nThere are {} contacts in the address-book\n'.format(len(ab))forname,addressinab.items():print'Contact {} at {}'.format(name,address)# Adding a key-value pairab['Guido']='guido@python.org'if'Guido'inab:print"\nGuido's address is",ab['Guido']

Output:

$ python ds_using_dict.py
Swaroop's address is swaroop@swaroopch.com
There are 3 contacts in the address-book
Contact Swaroop at swaroop@swaroopch.com
Contact Matsumoto at matz@ruby-lang.org
Contact Larry at larry@wall.org
Guido's address is guido@python.org

How It Works

We create the dictionary ab using the notation already discussed. We then access key-value pairs
by specifying the key using the indexing operator as discussed in the context of lists and
tuples. Observe the simple syntax.

We can delete key-value pairs using our old friend - the del statement. We simply specify the
dictionary and the indexing operator for the key to be removed and pass it to the del
statement. There is no need to know the value corresponding to the key for this operation.

Next, we access each key-value pair of the dictionary using the items method of the dictionary
which returns a list of tuples where each tuple contains a pair of items - the key followed by the
value. We retrieve this pair and assign it to the variables name and address correspondingly
for each pair using the for..in loop and then print these values in the for-block.

We can add new key-value pairs by simply using the indexing operator to access a key and assign
that value, as we have done for Guido in the above case.

We can check if a key-value pair exists using the in operator.

For the list of methods of the dict class, see help(dict).

Tip

Keyword Arguments and Dictionaries

If you have used keyword arguments in your functions, you have already used dictionaries! Just
think about it - the key-value pair is specified by you in the parameter list of the function
definition and when you access variables within your function, it is just a key access of a
dictionary (which is called the symbol table in compiler design terminology).

$ python ds_seq.py
Item 0 is apple
Item 1 is mango
Item 2 is carrot
Item 3 is banana
Item -1 is banana
Item -2 is carrot
Character 0 is s
Item 1 to 3 is ['mango', 'carrot']
Item 2 to end is ['carrot', 'banana']
Item 1 to -1 is ['mango', 'carrot']
Item start to end is ['apple', 'mango', 'carrot', 'banana']
characters 1 to 3 is wa
characters 2 to end is aroop
characters 1 to -1 is waroo
characters start to end is swaroop

How It Works

First, we see how to use indexes to get individual items of a sequence. This is also referred to as
the subscription operation. Whenever you specify a number to a sequence within square brackets as
shown above, Python will fetch you the item corresponding to that position in the
sequence. Remember that Python starts counting numbers from 0. Hence, shoplist[0] fetches the
first item and shoplist[3] fetches the fourth item in the `shoplist`sequence.

The index can also be a negative number, in which case, the position is calculated from the end of
the sequence. Therefore, shoplist[-1] refers to the last item in the sequence and shoplist[-2]
fetches the second last item in the sequence.

The slicing operation is used by specifying the name of the sequence followed by an optional pair
of numbers separated by a colon within square brackets. Note that this is very similar to the
indexing operation you have been using till now. Remember the numbers are optional but the colon
isn’t.

The first number (before the colon) in the slicing operation refers to the position from where the
slice starts and the second number (after the colon) indicates where the slice will stop at. If the
first number is not specified, Python will start at the beginning of the sequence. If the second
number is left out, Python will stop at the end of the sequence. Note that the slice returned
starts at the start position and will end just before the end position i.e. the start position
is included but the end position is excluded from the sequence slice.

Thus, shoplist[1:3] returns a slice of the sequence starting at position 1, includes position 2
but stops at position 3 and therefore a slice of two items is returned. Similarly, shoplist[:]
returns a copy of the whole sequence.

You can also do slicing with negative positions. Negative numbers are used for positions from the
end of the sequence. For example, shoplist[:-1] will return a slice of the sequence which
excludes the last item of the sequence but contains everything else.

You can also provide a third argument for the slice, which is the step for the slicing (by
default, the step size is 1):

Notice that when the step is 2, we get the items with position 0, 2,…​ When the step size is 3, we
get the items with position 0, 3, etc.

Try various combinations of such slice specifications using the Python interpreter interactively
i.e. the prompt so that you can see the results immediately. The great thing about sequences is
that you can access tuples, lists and strings all in the same way!

Sets are unordered collections of simple objects. These are used when the existence of an object
in a collection is more important than the order or how many times it occurs.

Using sets, you can test for membership, whether it is a subset of another set, find the
intersection between two sets, and so on.

>>>bri=set(['brazil','russia','india'])>>>'india'inbriTrue>>>'usa'inbriFalse>>>bric=bri.copy()>>>bric.add('china')>>>bric.issuperset(bri)True>>>bri.remove('russia')>>>bri&bric# OR bri.intersection(bric){'brazil','india'}

How It Works

The example is pretty much self-explanatory because it involves basic set theory mathematics taught
in school.

When you create an object and assign it to a variable, the variable only refers to the object and
does not represent the object itself! That is, the variable name points to that part of your
computer’s memory where the object is stored. This is called binding the name to the object.

Generally, you don’t need to be worried about this, but there is a subtle effect due to references
which you need to be aware of:

Example (save as ds_reference.py):

print'Simple Assignment'shoplist=['apple','mango','carrot','banana']# mylist is just another name pointing to the same object!mylist=shoplist# I purchased the first item, so I remove it from the listdelshoplist[0]print'shoplist is',shoplistprint'mylist is',mylist# Notice that both shoplist and mylist both print# the same list without the 'apple' confirming that# they point to the same objectprint'Copy by making a full slice'# Make a copy by doing a full slicemylist=shoplist[:]# Remove first itemdelmylist[0]print'shoplist is',shoplistprint'mylist is',mylist# Notice that now the two lists are different

Remember that if you want to make a copy of a list or such kinds of sequences or complex objects
(not simple objects such as integers), then you have to use the slicing operation to make a
copy. If you just assign the variable name to another name, both of them will ''refer'' to the same
object and this could be trouble if you are not careful.

Note

Note for Perl programmers

Remember that an assignment statement for lists does not create a copy. You have to use slicing
operation to make a copy of the sequence.

We have already discussed strings in detail earlier. What more can there be to know? Well, did you
know that strings are also objects and have methods which do everything from checking part of a
string to stripping spaces!

The strings that you use in program are all objects of the class str. Some useful methods of
this class are demonstrated in the next example. For a complete list of such methods, see
help(str).

Example (save as ds_str_methods.py):

# This is a string objectname='Swaroop'ifname.startswith('Swa'):print'Yes, the string starts with "Swa"'if'a'inname:print'Yes, it contains the string "a"'ifname.find('war')!=-1:print'Yes, it contains the string "war"'delimiter='_*_'mylist=['Brazil','Russia','India','China']printdelimiter.join(mylist)

Here, we see a lot of the string methods in action. The startswith method is used to find out
whether the string starts with the given string. The in operator is used to check if a given
string is a part of the string.

The find method is used to locate the position of the given substring within the string; find
returns -1 if it is unsuccessful in finding the substring. The str class also has a neat method
to join the items of a sequence with the string acting as a delimiter between each item of the
sequence and returns a bigger string generated from this.

We have explored various parts of the Python language and now we will take a look at how all these
parts fit together, by designing and writing a program which does something useful. The idea is
to learn how to write a Python script on your own.

Although, this is a simple problem, there is not enough information for us to get started with the
solution. A little more analysis is required. For example, how do we specify which files are to
be backed up? How are they stored? Where are they stored?

After analyzing the problem properly, we design our program. We make a list of things about how
our program should work. In this case, I have created the following list on how I want it to
work. If you do the design, you may not come up with the same kind of analysis since every person
has their own way of doing things, so that is perfectly okay.

The files and directories to be backed up are specified in a list.

The backup must be stored in a main backup directory.

The files are backed up into a zip file.

The name of the zip archive is the current date and time.

We use the standard zip command available by default in any standard GNU/Linux or Unix
distribution. Note that you can use any archiving command you
want as long as it has a command line interface.

As the design of our program is now reasonably stable, we can write the code which is an
implementation of our solution.

Save as backup_ver1.py:

importosimporttime# 1. The files and directories to be backed up are# specified in a list.# Example on Windows:# source = ['"C:\\My Documents"', 'C:\\Code']# Example on Mac OS X and Linux:source=['/Users/swa/notes']# Notice we had to use double quotes inside the string# for names with spaces in it.# 2. The backup must be stored in a# main backup directory# Example on Windows:# target_dir = 'E:\\Backup'# Example on Mac OS X and Linux:target_dir='/Users/swa/backup'# Remember to change this to which folder you will be using# 3. The files are backed up into a zip file.# 4. The name of the zip archive is the current date and timetarget=target_dir+os.sep+ \
time.strftime('%Y%m%d%H%M%S')+'.zip'# Create target directory if it is not presentifnotos.path.exists(target_dir):os.mkdir(target_dir)# make directory# 5. We use the zip command to put the files in a zip archivezip_command="zip -r {0} {1}".format(target,' '.join(source))# Run the backupprint"Zip command is:"printzip_commandprint"Running:"ifos.system(zip_command)==0:print'Successful backup to',targetelse:print'Backup FAILED'

Now, we are in the testing phase where we test that our program works properly. If it doesn’t
behave as expected, then we have to debug our program i.e. remove the bugs (errors) from the
program.

If the above program does not work for you, copy the line printed after the Zip command is line
in the output, paste it in the shell (on GNU/Linux and Mac OS X) / cmd (on Windows), see what the
error is and try to fix it. Also check the zip command manual on what could be wrong. If this
command succeeds, then the problem might be in the Python program itself, so check if it exactly
matches the program written above.

How It Works

You will notice how we have converted our design into code in a step-by-step manner.

We make use of the os and time modules by first importing them. Then, we specify the files and
directories to be backed up in the source list. The target directory is where we store all the
backup files and this is specified in the target_dir variable. The name of the zip archive that
we are going to create is the current date and time which we generate using the time.strftime()
function. It will also have the .zip extension and will be stored in the target_dir directory.

Notice the use of the os.sep variable - this gives the directory separator according to your
operating system i.e. it will be '/' in GNU/Linux and Unix, it will be '\\' in Windows and
':' in Mac OS. Using os.sep instead of these characters directly will make our program portable
and work across all of these systems.

The time.strftime() function takes a specification such as the one we have used in the above
program. The %Y specification will be replaced by the year with the century. The %m
specification will be replaced by the month as a decimal number between 01 and 12 and
so on. The complete list of such specifications can be found in the
Python Reference Manual.

We create the name of the target zip file using the addition operator which concatenates the
strings i.e. it joins the two strings together and returns a new one. Then, we create a string
zip_command which contains the command that we are going to execute. You can check if this
command works by running it in the shell (GNU/Linux terminal or DOS prompt).

The zip command that we are using has some options and parameters passed. The -r option
specifies that the zip command should work recursively for directories i.e. it should include
all the subdirectories and files. The two options are combined and specified in a shortcut as
-qr. The options are followed by the name of the zip archive to create followed by the list of
files and directories to backup. We convert the source list into a string using the join method
of strings which we have already seen how to
use.

Then, we finally run the command using the os.system function which runs the command as if it
was run from the system i.e. in the shell - it returns 0 if the command was successfully, else
it returns an error number.

Depending on the outcome of the command, we print the appropriate message that the backup has
failed or succeeded.

That’s it, we have created a script to take a backup of our important files!

Note

Note to Windows Users

Instead of double backslash escape sequences, you can also use raw strings. For example, use
'C:\\Documents' or r’C:\Documents'. However, do not use 'C:\Documents' since you end up
using an unknown escape sequence \D.

Now that we have a working backup script, we can use it whenever we want to take a backup of the
files. This is called the operation phase or the deployment phase of the software.

The above program works properly, but (usually) first programs do not work exactly as you
expect. For example, there might be problems if you have not designed the program properly or if
you have made a mistake when typing the code, etc. Appropriately, you will have to go back to the
design phase or you will have to debug your program.

The first version of our script works. However, we can make some refinements to it so that it can
work better on a daily basis. This is called the maintenance phase of the software.

One of the refinements I felt was useful is a better file-naming mechanism - using the time as
the name of the file within a directory with the current date as a directory within the main
backup directory. The first advantage is that your backups are stored in a hierarchical manner and
therefore it is much easier to manage. The second advantage is that the filenames are much
shorter. The third advantage is that separate directories will help you check if you have made a
backup for each day since the directory would be created only if you have made a backup for
that day.

Save as backup_ver2.py:

importosimporttime# 1. The files and directories to be backed up are# specified in a list.# Example on Windows:# source = ['"C:\\My Documents"', 'C:\\Code']# Example on Mac OS X and Linux:source=['/Users/swa/notes']# Notice we had to use double quotes inside the string# for names with spaces in it.# 2. The backup must be stored in a# main backup directory# Example on Windows:# target_dir = 'E:\\Backup'# Example on Mac OS X and Linux:target_dir='/Users/swa/backup'# Remember to change this to which folder you will be using# Create target directory if it is not presentifnotos.path.exists(target_dir):os.mkdir(target_dir)# make directory# 3. The files are backed up into a zip file.# 4. The current day is the name of the subdirectory# in the main directory.today=target_dir+os.sep+time.strftime('%Y%m%d')# The current time is the name of the zip archive.now=time.strftime('%H%M%S')# The name of the zip filetarget=today+os.sep+now+'.zip'# Create the subdirectory if it isn't already thereifnotos.path.exists(today):os.mkdir(today)print'Successfully created directory',today# 5. We use the zip command to put the files in a zip archivezip_command="zip -r {0} {1}".format(target,' '.join(source))# Run the backupprint"Zip command is:"printzip_commandprint"Running:"ifos.system(zip_command)==0:print'Successful backup to',targetelse:print'Backup FAILED'

Most of the program remains the same. The changes are that we check if there is a directory with
the current day as its name inside the main backup directory using the os.path.exists
function. If it doesn’t exist, we create it using the os.mkdir function.

The second version works fine when I do many backups, but when there are lots of backups, I am
finding it hard to differentiate what the backups were for! For example, I might have made some
major changes to a program or presentation, then I want to associate what those changes are with
the name of the zip archive. This can be easily achieved by attaching a user-supplied comment to
the name of the zip archive.

Warning

The following program does not work, so do not be alarmed, please follow along because
there’s a lesson in here.

Save as backup_ver3.py:

importosimporttime# 1. The files and directories to be backed up are# specified in a list.# Example on Windows:# source = ['"C:\\My Documents"', 'C:\\Code']# Example on Mac OS X and Linux:source=['/Users/swa/notes']# Notice we had to use double quotes inside the string# for names with spaces in it.# 2. The backup must be stored in a# main backup directory# Example on Windows:# target_dir = 'E:\\Backup'# Example on Mac OS X and Linux:target_dir='/Users/swa/backup'# Remember to change this to which folder you will be using# Create target directory if it is not presentifnotos.path.exists(target_dir):os.mkdir(target_dir)# make directory# 3. The files are backed up into a zip file.# 4. The current day is the name of the subdirectory# in the main directory.today=target_dir+os.sep+time.strftime('%Y%m%d')# The current time is the name of the zip archive.now=time.strftime('%H%M%S')# Take a comment from the user to# create the name of the zip filecomment=raw_input('Enter a comment --> ')# Check if a comment was enterediflen(comment)==0:target=today+os.sep+now+'.zip'else:target=today+os.sep+now+'_'+comment.replace(' ','_')+'.zip'# Create the subdirectory if it isn't already thereifnotos.path.exists(today):os.mkdir(today)print'Successfully created directory',today# 5. We use the zip command to put the files in a zip archivezip_command="zip -r {0} {1}".format(target,' '.join(source))# Run the backupprint"Zip command is:"printzip_commandprint"Running:"ifos.system(zip_command)==0:print'Successful backup to',targetelse:print'Backup FAILED'

This program does not work! Python says there is a syntax error which means that the script does
not satisfy the structure that Python expects to see. When we observe the error given by Python, it
also tells us the place where it detected the error as well. So we start debugging our program
from that line.

On careful observation, we see that the single logical line has been split into two physical lines
but we have not specified that these two physical lines belong together. Basically, Python has
found the addition operator (+) without any operand in that logical line and hence it doesn’t
know how to continue. Remember that we can specify that the logical line continues in the next
physical line by the use of a backslash at the end of the physical line. So, we make this
correction to our program. This correction of the program when we find errors is called bug
fixing.

importosimporttime# 1. The files and directories to be backed up are# specified in a list.# Example on Windows:# source = ['"C:\\My Documents"', 'C:\\Code']# Example on Mac OS X and Linux:source=['/Users/swa/notes']# Notice we had to use double quotes inside the string# for names with spaces in it.# 2. The backup must be stored in a# main backup directory# Example on Windows:# target_dir = 'E:\\Backup'# Example on Mac OS X and Linux:target_dir='/Users/swa/backup'# Remember to change this to which folder you will be using# Create target directory if it is not presentifnotos.path.exists(target_dir):os.mkdir(target_dir)# make directory# 3. The files are backed up into a zip file.# 4. The current day is the name of the subdirectory# in the main directory.today=target_dir+os.sep+time.strftime('%Y%m%d')# The current time is the name of the zip archive.now=time.strftime('%H%M%S')# Take a comment from the user to# create the name of the zip filecomment=raw_input('Enter a comment --> ')# Check if a comment was enterediflen(comment)==0:target=today+os.sep+now+'.zip'else:target=today+os.sep+now+'_'+ \
comment.replace(' ','_')+'.zip'# Create the subdirectory if it isn't already thereifnotos.path.exists(today):os.mkdir(today)print'Successfully created directory',today# 5. We use the zip command to put the files in a zip archivezip_command="zip -r {0} {1}".format(target,' '.join(source))# Run the backupprint"Zip command is:"printzip_commandprint"Running:"ifos.system(zip_command)==0:print'Successful backup to',targetelse:print'Backup FAILED'

This program now works! Let us go through the actual enhancements that we had made in version 3. We
take in the user’s comments using the input function and then check if the user actually entered
something by finding out the length of the input using the len function. If the user has just
pressed enter without entering anything (maybe it was just a routine backup or no special changes
were made), then we proceed as we have done before.

However, if a comment was supplied, then this is attached to the name of the zip archive just
before the .zip extension. Notice that we are replacing spaces in the comment with underscores -
this is because managing filenames without spaces is much easier.

The fourth version is a satisfactorily working script for most users, but there is always room for
improvement. For example, you can include a verbosity level for the program where you can specify
a -v option to make your program become more talkative or a -q to make it quiet.

Another possible enhancement would be to allow extra files and directories to be passed to the
script at the command line. We can get these names from the sys.argv list and we can add them to
our source list using the extend method provided by the list class.

The most important refinement would be to not use the os.system way of creating archives and
instead using the zipfile or
tarfile built-in modules to create these
archives. They are part of the standard library and available already for you to use without
external dependencies on the zip program to be available on your computer.

However, I have been using the os.system way of creating a backup in the above examples purely
for pedagogical purposes, so that the example is simple enough to be understood by everybody but
real enough to be useful.

Can you try writing the fifth version that uses the
zipfile module instead of the os.system call?

We have now gone through the various phases in the process of writing a software. These phases
can be summarised as follows:

What (Analysis)

How (Design)

Do It (Implementation)

Test (Testing and Debugging)

Use (Operation or Deployment)

Maintain (Refinement)

A recommended way of writing programs is the procedure we have
followed in creating the backup script: Do the analysis and
design. Start implementing with a simple version. Test and debug
it. Use it to ensure that it works as expected. Now, add any features that you want and continue to
repeat the Do It-Test-Use cycle as many times as required.

We have seen how to create our own Python programs/scripts and the various stages involved in
writing such programs. You may find it useful to create your own program just like we did in this
chapter so that you become comfortable with Python as well as problem-solving.

In all the programs we wrote till now, we have designed our program around functions i.e. blocks of
statements which manipulate data. This is called the procedure-oriented way of programming. There
is another way of organizing your program which is to combine data and functionality and wrap it
inside something called an object. This is called the object oriented programming paradigm. Most
of the time you can use procedural programming, but when writing large programs or have a problem
that is better suited to this method, you can use object oriented programming techniques.

Classes and objects are the two main aspects of object oriented programming. A class creates a
new type where objects are instances of the class. An analogy is that you can have variables
of type int which translates to saying that variables that store integers are variables which are
instances (objects) of the int class.

Note

Note for Static Language Programmers

Note that even integers are treated as objects (of the int class). This is unlike C++ and Java
(before version 1.5) where integers are primitive native types.

See help(int) for more details on the class.

C# and Java 1.5 programmers will find this similar to the boxing and unboxing concept.

Objects can store data using ordinary variables that belong to the object. Variables that belong
to an object or class are referred to as fields. Objects can also have functionality by using
functions that belong to a class. Such functions are called methods of the class. This
terminology is important because it helps us to differentiate between functions and variables which
are independent and those which belong to a class or object. Collectively, the fields and methods
can be referred to as the attributes of that class.

Fields are of two types - they can belong to each instance/object of the class or they can belong
to the class itself. They are called instance variables and class variables respectively.

A class is created using the class keyword. The fields and methods of the class are listed in an
indented block.

Class methods have only one specific difference from ordinary
functions - they must have an extra first name that has to be added to
the beginning of the parameter list, but you do not give a value
for this parameter when you call the method, Python will provide
it. This particular variable refers to the object itself, and by convention, it is given the name
self.

Although, you can give any name for this parameter, it is strongly recommended that you use the
name self - any other name is definitely frowned upon. There are many advantages to using a
standard name - any reader of your program will immediately recognize it and even specialized IDEs
(Integrated Development Environments) can help you if you use self.

Note

Note for C++/Java/C# Programmers

The self in Python is equivalent to the this pointer in C++ and the this reference in Java
and C#.

You must be wondering how Python gives the value for self and why you don’t need to give a value
for it. An example will make this clear. Say you have a class called MyClass and an instance of
this class called myobject. When you call a method of this object as myobject.method(arg1,
arg2), this is automatically converted by Python into MyClass.method(myobject, arg1, arg2) -
this is all the special self is about.

This also means that if you have a method which takes no arguments, then you still have to have one
argument - the self.

We create a new class using the class statement and the name of the class. This is followed by an
indented block of statements which form the body of the class. In this case, we have an empty block
which is indicated using the pass statement.

Next, we create an object/instance of this class using the name of the class followed by a pair of
parentheses. (We will learn more about instantiation in the next section). For our
verification, we confirm the type of the variable by simply printing it. It tells us that we have
an instance of the Person class in the main module.

Notice that the address of the computer memory where your object is stored is also printed. The
address will have a different value on your computer since Python can store the object wherever it
finds space.

There are many method names which have special significance in Python classes. We will see the
significance of the init method now.

The init method is run as soon as an object of a class is instantiated. The method is useful
to do any initialization you want to do with your object. Notice the double underscores both at
the beginning and at the end of the name.

Example (save as oop_init.py):

classPerson:def__init__(self,name):self.name=namedefsay_hi(self):print'Hello, my name is',self.namep=Person('Swaroop')p.say_hi()# The previous 2 lines can also be written as# Person('Swaroop').say_hi()

Output:

$ python oop_init.py
Hello, my name is Swaroop

How It Works

Here, we define the init method as taking a parameter name (along with the usual self).
Here, we just create a new field also called name. Notice these are two different variables even
though they are both called 'name'. There is no problem because the dotted notation self.name
means that there is something called "name" that is part of the object called "self" and the other
name is a local variable. Since we explicitly indicate which name we are referring to, there is
no confusion.

Most importantly, notice that we do not explicitly call the init method but pass the
arguments in the parentheses following the class name when creating a new instance of the
class. This is the special significance of this method.

Now, we are able to use the self.name field in our methods which is demonstrated in the sayHi
method.

We have already discussed the functionality part of classes and objects (i.e. methods), now let us
learn about the data part. The data part, i.e. fields, are nothing but ordinary variables that are
bound to the namespaces of the classes and objects. This means that these names are valid
within the context of these classes and objects only. That’s why they are called name spaces.

There are two types of fields - class variables and object variables which are classified
depending on whether the class or the object owns the variables respectively.

Class variables are shared - they can be accessed by all instances of that class. There is only
one copy of the class variable and when any one object makes a change to a class variable, that
change will be seen by all the other instances.

Object variables are owned by each individual object/instance of the class. In this case, each
object has its own copy of the field i.e. they are not shared and are not related in any way to the
field by the same name in a different instance. An example will make this easy to understand (save
as oop_objvar.py):

classRobot:"""Represents a robot, with a name."""# A class variable, counting the number of robotspopulation=0def__init__(self,name):"""Initializes the data."""self.name=nameprint"(Initializing {})".format(self.name)# When this person is created, the robot# adds to the populationRobot.population+=1defdie(self):"""I am dying."""print"{} is being destroyed!".format(self.name)Robot.population-=1ifRobot.population==0:print"{} was the last one.".format(self.name)else:print"There are still {:d} robots working.".format(Robot.population)defsay_hi(self):"""Greeting by the robot. Yeah, they can do that."""print"Greetings, my masters call me {}.".format(self.name)@classmethoddefhow_many(cls):"""Prints the current population."""print"We have {:d} robots.".format(cls.population)droid1=Robot("R2-D2")droid1.say_hi()Robot.how_many()droid2=Robot("C-3PO")droid2.say_hi()Robot.how_many()print"\nRobots can do some work here.\n"print"Robots have finished their work. So let's destroy them."droid1.die()droid2.die()Robot.how_many()

Output:

$ python oop_objvar.py
(Initializing R2-D2)
Greetings, my masters call me R2-D2.
We have 1 robots.
(Initializing C-3PO)
Greetings, my masters call me C-3PO.
We have 2 robots.
Robots can do some work here.
Robots have finished their work. So let's destroy them.
R2-D2 is being destroyed!
There are still 1 robots working.
C-3PO is being destroyed!
C-3PO was the last one.
We have 0 robots.

How It Works

This is a long example but helps demonstrate the nature of class and object variables. Here,
population belongs to the Robot class and hence is a class variable. The name variable belongs
to the object (it is assigned using self) and hence is an object variable.

Thus, we refer to the population class variable as Robot.population and not as
self.population. We refer to the object variable name using self.name notation in the methods
of that object. Remember this simple difference between class and object variables. Also note that
an object variable with the same name as a class variable will hide the class variable!

Instead of Robot.population, we could have also used self.__class__.population because every
object refers to it’s class via the self.__class__ attribute.

The how_many is actually a method that belongs to the class and not to the object. This means we
can define it as either a classmethod or a staticmethod depending on whether we need to know
which class we are part of. Since we refer to a class variable, let’s use classmethod.

We have marked the how_many method as a class method using a decorator.

Decorators can be imagined to be a shortcut to calling a wrapper function, so applying the
@classmethod decorator is same as calling:

how_many=classmethod(how_many)

Observe that the init method is used to initialize the Robot instance with a name. In this
method, we increase the population count by 1 since we have one more robot being added. Also
observe that the values of self.name is specific to each object which indicates the nature of
object variables.

Remember, that you must refer to the variables and methods of the same object using the selfonly. This is called an attribute reference.

In this program, we also see the use of docstrings for classes as well as methods. We can access
the class docstring at runtime using Robot.doc and the method docstring as
Robot.say_hi.doc

In the die method, we simply decrease the Robot.population count by 1.

All class members are public. One exception: If you use data members with names using the double
underscore prefix such as __privatevar, Python uses name-mangling to effectively make it a
private variable.

Thus, the convention followed is that any variable that is to be used only within the class or
object should begin with an underscore and all other names are public and can be used by other
classes/objects. Remember that this is only a convention and is not enforced by Python (except for
the double underscore prefix).

Note

Note for C++/Java/C# Programmers

All class members (including the data members) are public and all the methods are virtual in
Python.

One of the major benefits of object oriented programming is reuse of code and one of the ways
this is achieved is through the inheritance mechanism. Inheritance can be best imagined as
implementing a type and subtype relationship between classes.

Suppose you want to write a program which has to keep track of the teachers and students in a
college. They have some common characteristics such as name, age and address. They also have
specific characteristics such as salary, courses and leaves for teachers and, marks and fees for
students.

You can create two independent classes for each type and process them but adding a new common
characteristic would mean adding to both of these independent classes. This quickly becomes
unwieldy.

A better way would be to create a common class called SchoolMember and then have the teacher and
student classes inherit from this class i.e. they will become sub-types of this type (class) and
then we can add specific characteristics to these sub-types.

There are many advantages to this approach. If we add/change any functionality in SchoolMember,
this is automatically reflected in the subtypes as well. For example, you can add a new ID card
field for both teachers and students by simply adding it to the SchoolMember class. However,
changes in the subtypes do not affect other subtypes. Another advantage is that if you can refer to
a teacher or student object as a SchoolMember object which could be useful in some situations
such as counting of the number of school members. This is called polymorphism where a sub-type
can be substituted in any situation where a parent type is expected i.e. the object can be treated
as an instance of the parent class.

Also observe that we reuse the code of the parent class and we do not need to repeat it in the
different classes as we would have had to in case we had used independent classes.

The SchoolMember class in this situation is known as the base class or the superclass. The
Teacher and Student classes are called the derived classes or subclasses.

We will now see this example as a program (save as oop_subclass.py):

classSchoolMember:'''Represents any school member.'''def__init__(self,name,age):self.name=nameself.age=ageprint'(Initialized SchoolMember: {})'.format(self.name)deftell(self):'''Tell my details.'''print'Name:"{}" Age:"{}"'.format(self.name,self.age),classTeacher(SchoolMember):'''Represents a teacher.'''def__init__(self,name,age,salary):SchoolMember.__init__(self,name,age)self.salary=salaryprint'(Initialized Teacher: {})'.format(self.name)deftell(self):SchoolMember.tell(self)print'Salary: "{:d}"'.format(self.salary)classStudent(SchoolMember):'''Represents a student.'''def__init__(self,name,age,marks):SchoolMember.__init__(self,name,age)self.marks=marksprint'(Initialized Student: {})'.format(self.name)deftell(self):SchoolMember.tell(self)print'Marks: "{:d}"'.format(self.marks)t=Teacher('Mrs. Shrividya',40,30000)s=Student('Swaroop',25,75)# prints a blank lineprintmembers=[t,s]formemberinmembers:# Works for both Teachers and Studentsmember.tell()

To use inheritance, we specify the base class names in a tuple following the class name in the
class definition. Next, we observe that the init method of the base class is explicitly
called using the self variable so that we can initialize the base class part of the object. This
is very important to remember - Python does not automatically call the constructor of the base
class, you have to explicitly call it yourself.

We also observe that we can call methods of the base class by prefixing the class name to the
method call and then pass in the self variable along with any arguments.

Notice that we can treat instances of Teacher or Student as just instances of the
SchoolMember when we use the tell method of the SchoolMember class.

Also, observe that the tell method of the subtype is called and not the tell method of the
SchoolMember class. One way to understand this is that Python always starts looking for methods
in the actual type, which in this case it does. If it could not find the method, it starts looking
at the methods belonging to its base classes one by one in the order they are specified in the
tuple in the class definition.

A note on terminology - if more than one class is listed in the inheritance tuple, then it is
called multiple inheritance.

The trailing comma is used at the end of the print statement in the superclass’s tell() method
to print a line and allow the next print to continue on the same line. This is a trick to make
print not print a \n (newline) symbol at the end of the printing.

We have now explored the various aspects of classes and objects as well as the various
terminologies associated with it. We have also seen the benefits and pitfalls of object-oriented
programming. Python is highly object-oriented and understanding these concepts carefully will help
you a lot in the long run.

Next, we will learn how to deal with input/output and how to access files in Python.

There will be situations where your program has to interact with the user. For example, you would
want to take input from the user and then print some results back. We can achieve this using the
raw_input() function and print statement respectively.

For output, we can also use the various methods of the str (string) class. For example, you can
use the rjust method to get a string which is right justified to a specified width. See
help(str) for more details.

Another common type of input/output is dealing with files. The ability to create, read and write
files is essential to many programs and we will explore this aspect in this chapter.

defreverse(text):returntext[::-1]defis_palindrome(text):returntext==reverse(text)something=raw_input("Enter text: ")ifis_palindrome(something):print"Yes, it is a palindrome"else:print"No, it is not a palindrome"

We use the slicing feature to reverse the text. We’ve already seen how we can make
slices from sequences using the seq[a:b] code starting from position a to position
b. We can also provide a third argument that determines the step by which the slicing is
done. The default step is 1 because of which it returns a continuous part of the text. Giving a
negative step, i.e., -1 will return the text in reverse.

The raw_input() function takes a string as argument and displays it to the user. Then it waits
for the user to type something and press the return key. Once the user has entered and pressed the
return key, the raw_input() function will then return that text the user has entered.

We take that text and reverse it. If the original text and reversed text are equal, then the text
is a palindrome.

Checking whether a text is a palindrome should also ignore punctuation, spaces and case. For
example, "Rise to vote, sir." is also a palindrome but our current program doesn’t say it is. Can
you improve the above program to recognize this palindrome?

You can open and use files for reading or writing by creating an object of the file class and
using its read, readline or write methods appropriately to read from or write to the
file. The ability to read or write to the file depends on the mode you have specified for the file
opening. Then finally, when you are finished with the file, you call the close method to tell
Python that we are done using the file.

Example (save as io_using_file.py):

poem='''\Programming is funWhen the work is doneif you wanna make your work also fun: use Python!'''# Open for 'w'ritingf=open('poem.txt','w')# Write text to filef.write(poem)# Close the filef.close()# If no mode is specified,# 'r'ead mode is assumed by defaultf=open('poem.txt')whileTrue:line=f.readline()# Zero length indicates EOFiflen(line)==0:break# The `line` already has a newline# at the end of each line# since it is reading from a file.printline,# close the filef.close()

Output:

$ python io_using_file.py
Programming is fun
When the work is done
if you wanna make your work also fun:
use Python!

How It Works

First, open a file by using the built-in open function and specifying the name of the file and
the mode in which we want to open the file. The mode can be a read mode ('r'), write mode ('w')
or append mode ('a'). We can also specify whether we are reading, writing, or appending in text
mode ('t') or binary mode ('b'). There are actually many more modes available and help(open)
will give you more details about them. By default, open() considers the file to be a 't’ext file
and opens it in 'r’ead mode.

In our example, we first open the file in write text mode and use the write method of the file
object to write to the file and then we finally close the file.

Next, we open the same file again for reading. We don’t need to specify a mode because 'read text
file' is the default mode. We read in each line of the file using the readline method in a
loop. This method returns a complete line including the newline character at the end of the
line. When an empty string is returned, it means that we have reached the end of the file and we
'break' out of the loop.

In the end, we finally close the file.

Now, check the contents of the poem.txt file to confirm that the program has indeed written to
and read from that file.

Python provides a standard module called pickle using which you can store any plain Python
object in a file and then get it back later. This is called storing the object persistently.

Example (save as io_pickle.py):

importpickle# The name of the file where we will store the objectshoplistfile='shoplist.data'# The list of things to buyshoplist=['apple','mango','carrot']# Write to the filef=open(shoplistfile,'wb')# Dump the object to a filepickle.dump(shoplist,f)f.close()# Destroy the shoplist variabledelshoplist# Read back from the storagef=open(shoplistfile,'rb')# Load the object from the filestoredlist=pickle.load(f)printstoredlist

Output:

$ python io_pickle.py
['apple', 'mango', 'carrot']

How It Works

To store an object in a file, we have to first open the file in write binary mode and
then call the dump function of the pickle module. This process is called pickling.

Next, we retrieve the object using the load function of the pickle module which returns the
object. This process is called unpickling.

So far, when we have been writing and using strings, or reading and writing to a file, we have used
simple English characters only. If we want to be able to read and write other non-English
languages, we need to use the unicode type, and it all starts with the character u:

We use the unicode type instead of strings to make sure that we handle non-English languages in
our programs. However, when we read or write to a file or when we talk to other computers on the
Internet, we need to convert our unicode strings into a format that can be sent and received, and
that format is called "UTF-8". We can read and write in that format, using a simple keyword
argument to our standard open function:

You can ignore the import statement for now, we’ll explore that in detail in the modules
chapter.

Whenever we write a program that uses Unicode literals like we have used above, we have to make
sure that Python itself is told that our program uses UTF-8, and we have to put # encoding=utf-8
comment at the top of our program.

We use io.open and provide the "encoding" and "decoding" argument to tell Python that we are
using unicode, and in fact, we have to pass in a string in the form of u"" to make it clear that
we are using Unicode strings.

Exceptions occur when exceptional situations occur in your program. For example, what if you are
going to read a file and the file does not exist? Or what if you accidentally deleted it when the
program was running? Such situations are handled using exceptions.

Similarly, what if your program had some invalid statements? This is handled by Python which
raises its hands and tells you there is an error.

We put all the statements that might raise exceptions/errors inside the try block and then put
handlers for the appropriate errors/exceptions in the except clause/block. The except clause
can handle a single specified error or exception, or a parenthesized list of errors/exceptions. If
no names of errors or exceptions are supplied, it will handle all errors and exceptions.

Note that there has to be at least one except clause associated with every try
clause. Otherwise, what’s the point of having a try block?

If any error or exception is not handled, then the default Python handler is called which just
stops the execution of the program and prints an error message. We have already seen this in action
above.

You can also have an else clause associated with a try..except block. The else clause is
executed if no exception occurs.

In the next example, we will also see how to get the exception object so that we can retrieve
additional information.

You can raise exceptions using the raise statement by providing the name of the error/exception
and the exception object that is to be thrown.

The error or exception that you can raise should be a class which directly or indirectly must be a
derived class of the Exception class.

Example (save as exceptions_raise.py):

classShortInputException(Exception):'''A user-defined exception class.'''def__init__(self,length,atleast):Exception.__init__(self)self.length=lengthself.atleast=atleasttry:text=raw_input('Enter something --> ')iflen(text)<3:raiseShortInputException(len(text),3)# Other work can continue as usual hereexceptEOFError:print'Why did you do an EOF on me?'exceptShortInputExceptionasex:print('ShortInputException: The input was '+ \
'{0} long, expected at least {1}')\
.format(ex.length,ex.atleast)else:print'No exception was raised.'

Here, we are creating our own exception type. This new exception type is called
ShortInputException. It has two fields - length which is the length of the given input, and
atleast which is the minimum length that the program was expecting.

In the except clause, we mention the class of error which will be stored as the variable name
to hold the corresponding error/exception object. This is analogous to parameters and arguments in
a function call. Within this particular except clause, we use the length and atleast fields of
the exception object to print an appropriate message to the user.

Suppose you are reading a file in your program. How do you ensure that the file object is closed
properly whether or not an exception was raised? This can be done using the finally block.

Save this program as exceptions_finally.py:

importsysimporttimef=Nonetry:f=open("poem.txt")# Our usual file-reading idiomwhileTrue:line=f.readline()iflen(line)==0:breakprintline,sys.stdout.flush()print"Press ctrl+c now"# To make sure it runs for a whiletime.sleep(2)exceptIOError:print"Could not find file poem.txt"exceptKeyboardInterrupt:print"!! You cancelled the reading from the file."finally:iff:f.close()print"(Cleaning up: Closed the file)"

Output:

$ python exceptions_finally.py
Programming is fun
Press ctrl+c now
^C!! You cancelled the reading from the file.
(Cleaning up: Closed the file)

How It Works

We do the usual file-reading stuff, but we have arbitrarily introduced sleeping for 2 seconds after
printing each line using the time.sleep function so that the program runs slowly (Python is very
fast by nature). When the program is still running, press ctrl + c to interrupt/cancel the
program.

Observe that the KeyboardInterrupt exception is thrown and the program quits. However, before the
program exits, the finally clause is executed and the file object is always closed.

Note that we use sys.stdout.flush() after print so that it prints to the screen immediately.

Acquiring a resource in the try block and subsequently releasing the resource in the finally
block is a common pattern. Hence, there is also a with statement that enables this to be done in
a clean manner:

Save as exceptions_using_with.py:

withopen("poem.txt")asf:forlineinf:printline,

How It Works

The output should be same as the previous example. The difference here is that we are using the
open function with the with statement - we leave the closing of the file to be done
automatically by with open.

What happens behind the scenes is that there is a protocol used by the with statement. It fetches
the object returned by the open statement, let’s call it "thefile" in this case.

It always calls the thefile.enter function before starting the block of code under it and
always calls thefile.exit after finishing the block of code.

So the code that we would have written in a finally block should be taken care of automatically
by the exit method. This is what helps us to avoid having to use explicit try..finally
statements repeatedly.

More discussion on this topic is beyond scope of this book, so please refer
PEP 343 for a comprehensive explanation.

The Python Standard Library contains a huge number of useful modules and is part of every standard
Python installation. It is important to become familiar with the Python Standard Library since many
problems can be solved quickly if you are familiar with the range of things that these libraries
can do.

We will explore some of the commonly used modules in this library. You can find complete details
for all of the modules in the Python Standard Library in the
'Library Reference' section of the documentation that comes with
your Python installation.

Let us explore a few useful modules.

Caution

If you find the topics in this chapter too advanced, you may skip this chapter. However, I
highly recommend coming back to this chapter when you are more comfortable with programming using
Python.

What if you wanted to have some debugging messages or important messages to be stored somewhere so
that you can check whether your program has been running as you would expect it? How do you "store
somewhere" these messages? This can be achieved using the logging module.

If you do not have the cat command, then you can just open the test.log file in a text editor.

How It Works

We use three modules from the standard library - the os module for interacting with the operating
system, the platform module for information about the platform i.e. the operating system and the
logging module to log information.

First, we check which operating system we are using by checking the string returned by
platform.platform() (for more information, see import platform; help(platform)). If it is
Windows, we figure out the home drive, the home folder and the filename where we want to store the
information. Putting these three parts together, we get the full location of the file. For other
platforms, we need to know just the home folder of the user and we get the full location of the
file.

We use the os.path.join() function to put these three parts of the location together. The reason
to use a special function rather than just adding the strings together is because this function
will ensure the full location matches the format expected by the operating system.

We configure the logging module to write all the messages in a particular format to the file we
have specified.

Finally, we can put messages that are either meant for debugging, information, warning or even
critical messages. Once the program has run, we can check this file and we will know what happened
in the program, even though no information was displayed to the user running the program.

We have explored some of the functionality of many modules in the Python Standard Library. It is
highly recommended to browse through the Python Standard Library
documentation to get an idea of all the modules that are available.

Next, we will cover various aspects of Python that will make our tour of Python more complete.

There are certain methods such as the init and del methods which have special
significance in classes.

Special methods are used to mimic certain behaviors of built-in types. For example, if you want to
use the x[key] indexing operation for your class (just like you use it for lists and tuples),
then all you have to do is implement the getitem() method and your job is done. If you think
about it, this is what Python does for the list class itself!

Some useful special methods are listed in the following table. If you
want to know about all the special methods,
see the manual.

init(self, …​)

This method is called just before the newly created object is returned for usage.

del(self)

Called just before the object is destroyed (which has unpredictable timing, so avoid using this)

str(self)

Called when we use the print statement or when str() is used.

lt(self, other)

Called when the less than operator (<) is used. Similarly, there are special methods for all
the operators (+, >, etc.)

getitem(self, key)

Called when x[key] indexing operation is used.

len(self)

Called when the built-in len() function is used for the sequence object.

We have seen that each block of statements is set apart from the rest by its own indentation
level. Well, there is one caveat. If your block of statements contains only one single statement,
then you can specify it on the same line of, say, a conditional statement or looping statement. The
following example should make this clear:

>>> flag = True
>>> if flag: print 'Yes'
...
Yes

Notice that the single statement is used in-place and not as a separate block. Although, you can
use this for making your program smaller, I strongly recommend avoiding this short-cut method,
except for error checking, mainly because it will be much easier to add an extra statement if you
are using proper indentation.

A lambda statement is used to create new function objects. Essentially, the lambda takes a
parameter followed by a single expression only which becomes the body of the function and the value
of this expression is returned by the new function.

Notice that the sort method of a list can take a key parameter which determines how the list
is sorted (usually we know only about ascending or descending order). In our case, we want to do a
custom sort, and for that we need to write a function but instead of writing a separate def block
for a function that will get used in only this one place, we use a lambda expression to create a
new function.

List comprehensions are used to derive a new list from an existing list. Suppose you have a list of
numbers and you want to get a corresponding list with all the numbers multiplied by 2 only when the
number itself is greater than 2. List comprehensions are ideal for such situations.

Example (save as more_list_comprehension.py):

listone=[2,3,4]listtwo=[2*iforiinlistoneifi>2]printlisttwo

Output:

$ python more_list_comprehension.py
[6, 8]

How It Works

Here, we derive a new list by specifying the manipulation to be done (2*i) when some condition
is satisfied (if i > 2). Note that the original list remains unmodified.

The advantage of using list comprehensions is that it reduces the amount of boilerplate code
required when we use loops to process each element of a list and store it in a new list.

Because we have a * prefix on the args variable, all extra arguments passed to the function are
stored in args as a tuple. If a ** prefix had been used instead, the extra parameters would be
considered to be key/value pairs of a dictionary.

The assert statement is used to assert that something is true. For example, if you are very sure
that you will have at least one element in a list you are using and want to check this, and raise
an error if it is not true, then assert statement is ideal in this situation. When the assert
statement fails, an AssertionError is raised.

Decorators are a shortcut to applying wrapper functions. This is helpful to "wrap" functionality
with the same code over and over again. For example, I created a retry decorator for myself that
I can just apply to any function and if any exception is thrown during a run, it is retried again,
till a maximum of 5 times and with a delay between each retry. This is especially useful for
situations where you are trying to make a network call to a remote computer:

fromtimeimportsleepfromfunctoolsimportwrapsimportlogginglogging.basicConfig()log=logging.getLogger("retry")defretry(f):@wraps(f)defwrapped_f(*args,**kwargs):MAX_ATTEMPTS=5forattemptinrange(1,MAX_ATTEMPTS+1):try:returnf(*args,**kwargs)except:log.exception("Attempt %s/%s failed : %s",attempt,MAX_ATTEMPTS,(args,kwargs))sleep(10*attempt)log.critical("All %s attempts failed : %s",MAX_ATTEMPTS,(args,kwargs))returnwrapped_fcounter=0@retrydefsave_to_database(arg):print"Write to a database or make a network call or etc."print"This will be automatically retried if exception is thrown."globalcountercounter+=1# This will throw an exception in the first call# And will work fine in the second call (i.e. a retry)ifcounter<2:raiseValueError(arg)if__name__=='__main__':save_to_database("Some bad value")

Output:

$ python more_decorator.py
Write to a database or make a network call or etc.
This will be automatically retried if exception is thrown.
ERROR:retry:Attempt 1/5 failed : (('Some bad value',), {})
Traceback (most recent call last):
File "more_decorator.py", line 14, in wrapped_f
return f(*args, **kwargs)
File "more_decorator.py", line 39, in save_to_database
raise ValueError(arg)
ValueError: Some bad value
Write to a database or make a network call or etc.
This will be automatically retried if exception is thrown.

We have covered some more features of Python in this chapter and yet we haven’t covered all the
features of Python. However, at this stage, we have covered most of what you are ever going to use
in practice. This is sufficient for you to get started with whatever programs you are going to
create.

Next, we will discuss how to explore Python further.

[[What Next]]

If you have read this book thoroughly till now and practiced writing a lot of programs, then you
must have become comfortable and familiar with Python. You have probably created some Python
programs to try out stuff and to exercise your Python skills as well. If you have not done it
already, you should. The question now is 'What Next?'.

I would suggest that you tackle this problem:

Create your own command-line address-book program using which you can browse, add, modify, delete
or search for your contacts such as friends, family and colleagues and their information such as
email address and/or phone number. Details must be stored for later retrieval.

This is fairly easy if you think about it in terms of all the various stuff that we have come
across till now. If you still want directions on how to proceed, then here’s a hint
[3].

Once you are able to do this, you can claim to be a Python programmer. Now, immediately
send me an email thanking me for this great book ;-). This step is
optional but recommended. Also, please consider buying a printed
copy to support the continued development of this book.

If you found that program easy, here’s another one:

Implement the replace command. This command will
replace one string with another in the list of files provided.

The replace command can be as simple or as sophisticated as you wish, from simple string
substitution to looking for patterns (regular expressions).

Suppose you want to create your own graphical programs using Python. This can be done using a GUI
(Graphical User Interface) library with their Python bindings. Bindings are what allow you to write
programs in Python and use the libraries which are themselves written in C or C++ or other
languages.

This is the Python binding for the GTK+ toolkit which is the foundation upon which GNOME is
built. GTK+ has many quirks in usage but once you become comfortable, you can create GUI apps
fast. The Glade graphical interface designer is indispensable. The documentation is yet to
improve. GTK+ works well on GNU/Linux but its port to Windows is incomplete. You can create both
free as well as proprietary software using GTK+. To get started, read the
PyGTK tutorial.

PyQt

This is the Python binding for the Qt toolkit which is the foundation upon which the KDE is
built. Qt is extremely easy to use and very powerful especially due to the Qt Designer and the
amazing Qt documentation. PyQt is free if you want to create open source (GPL’ed) software and you
need to buy it if you want to create proprietary closed source software. Starting with Qt 4.5 you
can use it to create non-GPL software as well. To get started, read about
PySide.

wxPython

This is the Python bindings for the wxWidgets toolkit. wxPython has a learning curve associated
with it. However, it is very portable and runs on GNU/Linux, Windows, Mac and even embedded
platforms. There are many IDEs available for wxPython which include GUI designers as well such as
SPE (Stani’s Python Editor) and the wxGlade
GUI builder. You can create free as well as proprietary software using wxPython. To get started,
read the wxPython tutorial.

Unfortunately, there is no one standard GUI tool for Python. I suggest that you choose one of the
above tools depending on your situation. The first factor is whether you are willing to pay to use
any of the GUI tools. The second factor is whether you want the program to run only on Windows or
on Mac and GNU/Linux or all of them. The third factor, if GNU/Linux is a chosen platform, is
whether you are a KDE or GNOME user on GNU/Linux.

A Python implementation written in Python! This is a research project to make it fast and easy to
improve the interpreter since the interpreter itself is written in a dynamic language (as opposed
to static languages such as C, Java or C# in the above three implementations)

There are also others such as CLPython - a Python
implementation written in Common Lisp and Brython which is an implementation
on top of a JavaScript interpreter which could mean that you can use Python (instead of JavaScript)
to write your web-browser ("Ajax") programs.

Each of these implementations have their specialized areas where they are useful.

When you start writing larger programs, you should definitely learn more about a functional
approach to programming as opposed to the class-based approach to programming that we learned in
the object oriented programming chapter:

We have now come to the end of this book but, as they say, this is the the beginning of the
end!. You are now an avid Python user and you are no doubt ready to solve many problems using
Python. You can start automating your computer to do all kinds of previously unimaginable things or
write your own games and much much more. So, get started!

Please note that this section was written in 2003, so some of this might sound quaint to you
:-)

"Free/Libre and Open Source Software", in short, FLOSS is based
on the concept of a community, which itself is based on the concept of sharing, and particularly
the sharing of knowledge. FLOSS are free for usage, modification and redistribution.

If you have already read this book, then you are already familiar with FLOSS since you have been
using Python all along and Python is an open source software!

Here are some examples of FLOSS to give an idea of the kind of things that community sharing and
building can create:

This is a FLOSS OS kernel used in the GNU/Linux operating system. Linux, the kernel, was started by
Linus Torvalds as a student. Android is based on Linux. Any website you use these days will mostly
be running on Linux.

This is a community-driven distribution, sponsored by Canonical and it is the most popular
GNU/Linux distribution today. It allows you to install a plethora of FLOSS available and all this
in an easy-to-use and easy-to-install manner. Best of all, you can just reboot your computer and
run GNU/Linux off the CD! This allows you to completely try out the new OS before installing it on
your computer. However, Ubuntu is not entirely free software; it contains proprietary drivers,
firmware, and applications.

This is an excellent community-driven and developed office suite with a writer, presentation,
spreadsheet and drawing components among other things. It can even open and edit MS Word and MS
PowerPoint files with ease. It runs on almost all platforms and is entirely free, libre and open
source software.

This is the popular open source web server. In fact, it is the most popular web server on the
planet! It runs nearly more than half of the websites out there. Yes, that’s right - Apache handles
more websites than all the competition (including Microsoft IIS) combined.

Later, I switched to DocBook XML using Kate but I found it too tedious. So, I switched to
OpenOffice which was just excellent with the level of control it provided for formatting as well as
the PDF generation, but it produced very sloppy HTML from the document.

Finally, I discovered XEmacs and I rewrote the book from scratch in DocBook XML (again) after I
decided that this format was the long term solution.

In the sixth draft, I decided to use Quanta+ to do all the editing. The standard XSL stylesheets
that came with Fedora Core 3 Linux were being used. However, I had written a CSS document to give
color and style to the HTML pages. I had also written a crude lexical analyzer, in Python of
course, which automatically provides syntax highlighting to all the program listings.

For the seventh draft, I’m using MediaWiki as the basis of my setup. I
used to edit everything online and the readers can directly read/edit/discuss within the wiki
website, but I ended up spending more time fighting spam than writing.

I first started with Python when I needed to write an installer for software I had written called
'Diamond' so that I could make the installation easy. I had to choose between Python and Perl
bindings for the Qt library. I did some research on the web and I came across
an article by Eric S. Raymond, a famous and respected
hacker, where he talked about how Python had become his favorite programming language. I also found
out that the PyQt bindings were more mature compared to Perl-Qt. So, I decided that Python was the
language for me.

Then, I started searching for a good book on Python. I couldn’t find any! I did find some O’Reilly
books but they were either too expensive or were more like a reference manual than a guide. So, I
settled for the documentation that came with Python. However, it was too brief and small. It did
give a good idea about Python but was not complete. I managed with it since I had previous
programming experience, but it was unsuitable for newbies.

About six months after my first brush with Python, I installed the (then) latest Red Hat 9.0 Linux
and I was playing around with KWord. I got excited about it and suddenly got the idea of writing
some stuff on Python. I started writing a few pages but it quickly became 30 pages long. Then, I
became serious about making it more useful in a book form. After a lot of rewrites, it has
reached a stage where it has become a useful guide to learning the Python language. I consider
this book to be my contribution and tribute to the open source community.

This book started out as my personal notes on Python and I still consider it in the same way,
although I’ve taken a lot of effort to make it more palatable to others :)

In the true spirit of open source, I have received lots of constructive suggestions, criticisms and
feedback from enthusiastic readers which has helped me improve this book a lot.

In Dec 2008, the book was updated for the Python 3.0 release (one of the first books to do
so). But now, I have converted the book back for Python 2 language because readers would often
get confused between the default Python 2 installed on their systems vs. Python 3 which they had
to separately install and all the tooling, esp. editors would assume Python 2 as well. I had a
hard time justifying why I had to aggravate readers and make them go through all this when the
fact is that they can learn either one and it would be just as useful. So, Python 2 it is.

The book needs the help of its readers such as yourselves to point out any parts of the book which
are not good, not comprehensible or are simply wrong. Please write to the main author or
the respective translators with your comments and suggestions.

I am a postgraduate at Wireless Telecommunication Graduate School,
Beijing University of Technology, China PR. My current research
interest is on the synchronization, channel estimation and
multi-user detection of multicarrier CDMA system. Python is my major
programming language for daily simulation and research job, with the
help of Python Numeric, actually. I learned Python just half a year
before, but as you can see, it’s really easy-understanding,
easy-to-use and productive. Just as what is ensured in Swaroop’s
book, 'It’s my favorite programming language now'.

'A Byte of Python' is my tutorial to learn Python. It’s clear and
effective to lead you into a world of Python in the shortest time.
It’s not too long, but efficiently covers almost all important
things in Python. I think 'A Byte of Python' should be strongly
recommendable for newbies as their first Python tutorial. Just
dedicate my translation to the potential millions of Python users in
China.

An exciting feature of this translation is that it also contains the executable chinese python
sources side by side with the original python sources.

Fred Lin - I’m working as a network firmware engineer at Delta Network, and I’m also a contributor
of TurboGears web framework.

As a python evangelist (:-p), I need some material to promote python language. I found 'A Byte of
Python' hit the sweet point for both newbies and experienced programmers. 'A Byte of Python'
elaborates the python essentials with affordable size.

The translation are originally based on simplified chinese version, and soon a lot of rewrite were
made to fit the current wiki version and the quality of reading.

The recent chinese traditional version also featured with executable chinese python sources, which
are achieved by my new 'zhpy' (python in chinese) project (launch from Aug 07).

zhpy(pronounce (Z.H.?, or zippy) build a layer upon python to translate or interact with python in
chinese(Traditional or Simplified). This project is mainly aimed for education.

I’m 32 years old and have a degree of Mathematics from University of Heidelberg, Germany. Currently
I’m working as a software engineer on a publicly funded project to build a web portal for all
things related to computer science in Germany.The main language I use as a professional is Java,
but I try to do as much as possible with Python behind the scenes. Especially text analysis and
conversion is very easy with Python. I’m not very familiar with GUI toolkits, since most of my
programming is about web applications, where the user interface is build using Java frameworks like
Struts. Currently I try to make more use of the functional programming features of Python and of
generators. After taking a short look into Ruby, I was very impressed with the use of blocks in
this language. Generally I like the dynamic nature of languages like Python and Ruby since it
allows me to do things not possible in more static languages like Java.I’ve searched for some kind
of introduction to programming, suitable to teach a complete non-programmer. I’ve found the book
'How to Think Like a Computer Scientist: Learning with Python', and 'Dive into Python'. The first
is good for beginners but to long to translate. The second is not suitable for beginners. I think
'A Byte of Python' falls nicely between these, since it is not too long, written to the point, and
at the same time verbose enough to teach a newbie. Besides this, I like the simple DocBook
structure, which makes translating the text a generation the output in various formats a charm.

Bernd Hengelein says:

Lutz and me are going to do the german translation together. We just started with the intro and
preface but we will keep you informed about the progress we make. Ok, now some personal things
about me. I am 34 years old and playing with computers since the 1980’s, when the "Commodore C64"
ruled the nurseries. After studying computer science I started working as a software
engineer. Currently I am working in the field of medical imaging for a major german
company. Although C++ is the main language I (have to) use for my daily work, I am constantly
looking for new things to learn.Last year I fell in love with Python, which is a wonderful
language, both for its possibilities and its beauty. I read somewhere in the net about a guy who
said that he likes python, because the code looks so beautiful. In my opinion he’s absolutly
right. At the time I decided to learn python, I noticed that there is very little good
documentation in german available. When I came across your book the spontaneous idea of a german
translation crossed my mind. Luckily, Lutz had the same idea and we can now divide the work.I am
looking forward to a good cooperation!

Massimo Lucci and Enrico Morelli - we are working at the University of Florence (Italy) -
Chemistry Department. I (Massimo) as service engineer and system administrator for Nuclear Magnetic
Resonance Spectrometers; Enrico as service engineer and system administrator for our CED and
parallel / clustered systems. We are programming on python since about seven years, we had
experience working with Linux platforms since ten years. In Italy we are responsible and
administrator for www.gentoo.it web site for Gentoo/Linux distrubution and www.nmr.it (now under
construction) for Nuclear Magnetic Resonance applications and Congress Organization and
Managements.That’s all! We are impressed by the smart language used on your Book and we think this
is essential for approaching the Python to new users (we are thinking about hundred of students and
researcher working on our labs).

I am Jeongbin Park, currently working as a Biophysics & Bioinformatics researcher in Korea.

A year ago, I was looking for a good tutorial/guide for Python to introduce it to my colleagues,
because using Python in such research fields is becoming inevitable due to the user base is growing
more and more.

But at that time only few Python books are available in Korean, so I decided to translate your
ebook because it looks like one of the best guides that I have ever read!

Currently, the book is almost completely translated in Korean, except some of the text in
introduction chapter and the appendixes.

Eirik Vågeskar: I have always wanted to program, but because I speak a small language, the
learning process was much harder. Most tutorials and books are written in very technical English,
so most high school graduates will not even have the vocabulary to understand what the tutorial is
about. When I discovered this book, all my problems were solved. "A Byte of Python" used simple
non-technical language to explain a programming language that is just as simple, and these two
things make learning Python fun. After reading half of the book, I decided that the book was worth
translating. I hope the translation will help people who have found themself in the same situation
as me (especially young people), and maybe help spread interest for the language among people with
less technical knowledge.

Paul-Sebastian Manole - I’m a second year Computer Science student at Spiru Haret University,
here in Romania. I’m more of a self-taught programmer and decided to learn a new language,
Python. The web told me there was no better way to do so but read ''A Byte of Python''. That’s how
popular this book is (congratulations to the author for writing such an easy to read book). I
started liking Python so I decided to help translate the latest version of Swaroop’s book in
Romanian. Although I could be the one with the first initiative, I’m just one volunteer so if you
can help, please join me.

We are a non-profit organization called "Translation for education". We represent a group of
people, mainly students and professors, of the Slavonic University. Here are students from
different departments: linguistics, chemistry, biology, etc. We try to find interesting
publications on the Internet that can be relevant for us and our university colleagues. Sometimes
we find articles by ourselves; other times our professors help us choose the material for
translation. After obtaining permission from authors we translate articles and post them in our
blog which is available and accessible to our colleagues and friends. These translated publications
often help students in their daily study routine.

I work as a software engineer in Argentina. I use mostly C# and .Net technologies at work but
strictly Python or Ruby in my personal projects. I knew Python many years ago and I got stuck
inmediately. Not so long after knowing Python I discovered this book and it helped me to learn the
language. Then I volunteered to translate the book to Spanish. Now, after receiving some requests,
I’ve begun to translate "A Byte of Python" with the help of Maximiliano Soler.

Cristian Bermudez Serna says:

I am student of Telecommunications engineering at the University of Antioquia (Colombia). Months
ago, i started to learn Python and found this wonderful book, so i volunteered to get the Spanish
translation.

2. Use a tuple (you can find a list of allpunctuation marks here) to hold all the forbidden characters, then use the membership test to determine whether a character should be removed or not, i.e. forbidden = (!, ?, ., …​).

3. Create a class to represent the person’s information. Use a dictionary to store person objects with their name as the key. Use the pickle module to store the objects persistently on your hard disk. Use the dictionary built-in methods to add, delete and modify the persons.